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What is the direction of the linear trend of the given time series, if any?
[ "No Trend", "Upward", "Downward" ]
No Trend
multiple_choice
4
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend" ]
Check if the time series values increase or decrease over time.
Pattern Recognition
Trend Recognition
101
[ 3.9964851848365117, 4.096726242468302, 3.9889441590272905, 4.070670467240246, 3.973131000184826, 4.038370474465647, 4.0307017913405225, 4.08391597369271, 3.9924638530153373, 3.981361525798657, 4.014080436726348, 4.0856671475157205, 3.9832304810571806, 3.914740389911657, 4.021164639929741, 4.056921785339795, 3.9780202575706958, 4.052839094376282, 3.9771319079683964, 4.012872872583226, 4.049011228596695, 3.971041295580067, 4.0260573024519966, 4.067589614804349, 4.037808999227695, 3.989802461069334, 4.079796399232258, 4.0452219451375555, 3.943995721863452, 4.053494612500351, 3.9958005369631815, 4.026662134926157, 4.059272001431102, 3.996675408253423, 3.952630249996811, 4.079267001157547, 4.0655645586624525, 4.0152561002453, 4.0530596954393046, 4.003917936114787, 4.0226597136731765, 4.012964122330268, 4.005543230120518, 4.033202230480455, 3.970208872304007, 4.008652275749124, 4.031400896145608, 4.092631618234718, 3.9718422826682773, 4.039682504549634, 4.044086896426918, 3.9433418398027027, 4.035261019884805, 4.0541392808841294, 4.042419266751768, 4.015389875143058, 4.032154838697838, 4.025588367729372, 3.950867430813829, 4.026531797494055, 4.004011023763668, 3.9904375817708297, 4.118627758275669, 4.069102365836718, 3.9725400264794124, 4.032693198582551, 4.077357288478666, 3.958634018660632, 4.021856482149075, 4.0237489008048275, 3.967543867633748, 4.034015938483829, 4.063622006087685, 4.079367566656103, 4.008741360833222, 4.119297890235195, 3.9360579118489682, 4.033078216676641, 3.9620059548491713, 4.02238693857615, 3.9977885185008457, 4.039132914582215, 4.013663677539871, 3.985568368210811, 3.988208423261189, 4.0964236740825, 4.036586878989221, 3.9503581985925287, 4.04089756449056, 4.01093911925087, 4.018947619140018, 4.051712071782733, 4.066313804827166, 3.984249066688653, 4.017567465860157, 3.9968158070212016, 4.04274918714463, 4.009426945993972, 4.0199382599264695, 4.007900930675724, 4.01449802084515, 4.0872685913696305, 4.114841931536977, 4.033917148822003, 4.014745164132478, 3.9546378571959337, 3.98820330929532, 4.013669491818098, 3.9900260182865646, 3.994252247926654, 4.082073877125325, 4.019919746854504, 4.023513371799403, 3.9916685935182215, 4.066085440687885, 4.0102196817313285, 3.9978304376100424, 3.9552360879459205, 4.016307171441012, 3.9965768112413245, 4.086461975060415, 4.092864625455399, 3.9691197204505806, 3.9736660364678102, 4.007658081903983, 3.9859680857085023, 4.027500168641598, 4.005636489378191 ]
null
What is the primary cyclic pattern observed in the time series?
[ "SineWave", "No Pattern at all", "SquareWave", "SawtoothWave" ]
SineWave
multiple-choice
15
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Square Wave", "Sawtooth Wave" ]
Check the overall shape of the time series against the definition of provided concepts
Pattern Recognition
Cycle Recognition
102
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null
You are given two time series where one is the lagged version of the other. What is the most likely lagging step?
[ "Lagging step is between 30 to 45", "Lagging step is between 60 to 75", "Lagging step is between 5 to 20" ]
Lagging step is between 30 to 45
multiple_choice
100
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Lagged Pair" ]
You already know that one time series is the lagged version of the other. Shift the time series by lags proposed in the options and check which one looks the same as the other time series.
Causality Analysis
Granger Causality
103
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Two time series are given with different cyclic components. Which time series has a higher period of the cyclic component?
[ "Time series 2 has higher period", "Time series 1 has higher period" ]
Time series 1 has higher period
binary
85
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Square Wave", "Period" ]
Period refers to the length of one cycle in the cyclic component. You should check the distance between two peaks or two troughs for both time series.
Similarity Analysis
Shape
104
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The given time series has an increasing trend, is it a linear trend or log trend?
[ "Linear", "Log" ]
Linear
multiple_choice
7
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend", "Log Trend" ]
Check if the slope of the time series is constant or changes over time.
Pattern Recognition
Trend Recognition
105
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null
Given that following time series exhibit piecewise linear trend, how many pieces are there?
[ "4", "1", "2" ]
1
multiple_choice
5
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Piecewise Linear Trend" ]
Check if the time series values increase or decrease linearly over time with different slopes. The slope change could be both positive and negative.
Pattern Recognition
Trend Recognition
106
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null
You are given two time series following similar pattern. Both of them have an anomaly. What is the likely type of anomaly in each time series?
[ "Time series 1 with flip anomaly and time series 2 with speed up/down anomaly", "Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly", "Time series 1 with cutoff anomaly and time series 2 with flip anomaly" ]
Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly
multiple_choice
75
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Sawtooth Wave", "Linear Trend", "Log Trend", "Cutoff Anomaly", "Flip Anomaly", "Speed Up/Down Anomaly" ]
You already know both time series have an anomaly. You should treat them separately and check the type of anomaly based on the given definitions.
Anolmaly Detection
General Anomaly Detection
107
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You are given two time series following similar pattern. One has an anomaly and the other does not. Which time series has the anomaly, and what is the likely type of anomaly?
[ "Time series 1 with flip anomaly", "Time series 1 with speed up/down anomaly", "Time series 2 with cutoff anomaly" ]
Time series 1 with speed up/down anomaly
multiple_choice
74
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Linear Trend", "Speed Up/Down Anomaly", "Cutoff Anomaly", "Flip Anomaly" ]
You should first identify the time series with the anomaly. Remember, both time series share similar pattern. Then, you should check the type of anomaly based on the given definitions.
Anolmaly Detection
General Anomaly Detection
108
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The time series has a trend and cyclic component added together. Which components are most likely present in the given time series?
[ "No trend and sawtooth wave", "Linear trend and sine wave", "Exponential trend and sine wave" ]
Linear trend and sine wave
multiple-choice
26
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend", "Exponential Trend", "Sine Wave", "Sawtooth Wave", "Additive Composition" ]
For trend, check if the slope is constant or changes over time. For cyclic component, check the overall shape of the time series.
Pattern Recognition
Cycle Recognition
109
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null
The given time series is a swatooth wave followed by a square wave. What is the most likely period of the swatooth wave?
[ "11.56", "59.37", "33.87" ]
11.56
multiple-choice
26
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sawtooth Wave", "Square Wave", "Period" ]
The sawtooth wave comes before the square wave. Begin by identifying where the sawtooth wave starts. Next, measure the time interval between two peaks.
Pattern Recognition
Cycle Recognition
110
[ -1.154115999334134, -1.0082966626019227, -0.9049904287430496, -0.5767898404232986, -0.45039046869548194, -0.22362452940360217, 0.0741418184776863, 0.08561471222296246, 0.3314480988050983, 0.6747082744999637, 0.8566510607582777, 1.2163560514189067, -1.3029485057948285, -1.126379323008577, -0.6115869306152333, -0.5537322921883829, -0.2974046963032774, -0.21963519002575635, 0.09539850151073064, 0.38792617830189463, 0.4887317069563371, 0.8683980119007939, 0.9977614864420054, 1.2556531258222174, -1.1724255768234813, -0.7000299517819883, -0.6596103473390162, -0.5370345723931536, -0.12201184291958533, -0.09935355375934712, 0.2706143161058898, 0.2807580895850963, 0.5709036424508903, 0.9504055162953049, 1.2315631960755926, -1.222224050207038, -1.0239285611971998, -0.8154769571496452, -0.7062216412597997, -0.4033567176948686, -0.15043902858371722, 0.22833421650181246, 0.3839809682044264, 0.400312269078966, 0.8360218269223787, 0.9921023473089267, 1.1899155206876215, -1.0783020667997825, -0.8093725980666148, -0.5923473930369306, -0.5424000119031551, -0.26240235179835325, 0.028642374294783723, 0.3200676892343226, 0.40159289810521703, 0.6579415695910235, 0.7928711152244617, 1.010881095584657, -1.185323076169943, -0.9039545101846211, -0.8197823798320775, -0.4852309325171823, -0.3224234744339621, -0.19610190703157868, -0.09545037102022486, 1.1383809494119297, 0.9809946888543375, 1.1410416583467333, 0.7226027823563582, 1.0667675432028552, 0.9932819995891499, 0.9546765577187459, 0.9937533704188829, 0.7858204013052434, 0.9626101039815815, 1.0202885499165073, 1.1323666972394844, 0.9327502709379679, 0.9037279324760139, 0.934401588406879, 1.07611750453554, 1.017452403731301, 0.9316012723886288, 1.0359040360766683, 0.9942850477001367, 1.0814417918186217, 0.9143719833775974, -1.280523370567242, -1.286967971220681, -1.394108650720677, -1.2181451282010076, -1.2216516286894763, -1.2472458102432191, -1.27121586924498, -1.3892942301125066, -1.289821688184001, -1.2820286075601421, -1.327984882829627, -1.263885727074066, -1.2073520702260114, -1.059138565786412, -1.2302993746242812, -1.2220021168351887, -1.255201747484082, -1.4396342774373694, -1.2504085434523868, -1.2417341349133626, -1.0014329446589365, -1.2669932523855774, -1.217602421674104, 0.9811061157948083, 0.8677094890033794, 1.0988595742168348, 1.05977059603401, 1.0636804874696373, 0.8936385472858588, 1.1248567238589426, 0.8443921864861046, 1.0432630021453597, 1.2036228553463306, 0.8855236602522638, 0.9279475198050555, 0.9945424292740968, 0.9342297273537128, 0.8295109496587194, 0.9914335902459354, 0.8783469213927222, 1.0319365358288508 ]
null
The following time series has an anomaly. What is the most likely type of anomaly?
[ "Wander: the pattern deviates off for certain point in time", "Cutoff: the pattern of time series disappeared for certain point in time", "Scale: the pattern is at obviously different scale at certain point in time" ]
Wander: the pattern deviates off for certain point in time
multiple_choice
65
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Cutoff Anomaly", "Scale Anomaly", "Wander Anomaly" ]
Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions.
Anolmaly Detection
General Anomaly Detection
111
[ -2.5930859737204655, -2.3196643794326692, -2.046242785144873, -1.7728211908570768, -1.4993995965692806, -1.2259780022814843, -0.952556407993688, -0.6791348137058919, -0.40571321941809557, -0.13229162513029924, 0.14112996915749687, 0.414551563445293, 0.6879731577330894, 0.9613947520208855, 1.2348163463086816, 1.5082379405964783, 1.7816595348842743, 2.0550811291720703, 2.328502723459867, 2.601924317747663, 2.875345912035459, 3.1487675063232556, 3.4221891006110514, 3.695610694898848, 3.9690322891866443, 4.242453883474441, -0.6702964696786944, -0.39687487539089816, -0.12345328110310172, 0.1499683131846945, 0.4233899074724907, 0.6968115017602867, 0.9702330960480832, 1.2436546903358794, 1.5170762846236754, 1.7904978789114718, 2.063919473199268, 2.3373410674870643, 2.6107626617748605, 2.879099510299945, 3.1474363588250296, 3.4157732073501137, 3.6841100558751982, 3.9524469044002823, 4.220783752925367, 4.489120601450451, 4.757457449975535, 5.02579429850062, 5.294131147025705, 5.562467995550788, 5.830804844075873, 6.099141692600957, 1.1813065936851106, 1.4496434422101954, 1.7179802907352797, 1.986317139260364, 2.2546539877854483, 2.522990836310533, 2.791327684835617, 3.0596645333607015, 3.328001381885786, 3.5963382304108698, 3.8646750789359543, 4.133011927461038, 4.401348775986123, 4.669685624511208, 4.938022473036292, 5.206359321561377, 5.474696170086461, 5.743033018611545, 6.011369867136629, 6.279706715661713, 6.5480435641867984, 6.816380412711883, 7.084717261236967, 7.353054109762051, 7.621390958287136, 2.703555859371289, 2.9718927078963735, 3.240229556421458, 3.5085664049465426, 3.7769032534716267, 4.045240101996711, 4.313576950521795, 4.5819137990468795, 4.850250647571964, 5.1185874960970486, 5.386924344622133, 5.655261193147218, 5.923598041672302, 6.191934890197386, 6.46027173872247, 6.728608587247554, 6.9969454357726395, 7.265282284297724, 7.533619132822809, 7.801955981347892, 8.070292829872976, 8.343714424160773, 8.61713601844857, 8.890557612736366, 9.16397920702416, 9.437400801311957, 4.524650448158824, 4.798072042446619, 5.071493636734416, 5.344915231022212, 5.618336825310008, 5.891758419597805, 6.1651800138856006, 6.438601608173397, 6.712023202461193, 6.985444796748989, 7.258866391036785, 7.532287985324582, 7.805709579612377, 8.079131173900175, 8.35255276818797, 8.625974362475766, 8.899395956763563, 9.17281755105136, 9.446239145339154, 9.71966073962695, 9.993082333914746, 10.266503928202543, 10.53992552249034, 10.813347116778136, 11.086768711065933 ]
null
Two time series are given, one with an upward trend and the other with a downward trend. Do they exhibit similar patterns aside from the trend?
[ "No, they have different cyclic components", "Yes, they share a similar pattern" ]
No, they have different cyclic components
binary
90
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend", "Sine Wave", "Square Wave" ]
You should focus on the cyclic components of the time series. Do they have similar patterns aside from the trend?
Similarity Analysis
Shape
112
[ -0.014987796676246168, 0.4156863181633759, 0.8002326503662567, 0.9206971846611812, 1.3279039894920226, 1.634785838836855, 1.9284093069911918, 1.9959463673543103, 2.2088536799458107, 2.3140082582338115, 2.2814059620697122, 2.267935587478473, 2.2895999624745182, 2.159120018753523, 2.0728829185547113, 1.732436828280551, 1.5889047407373158, 1.3664155166931362, 1.2237197273773237, 0.79775799573148, 0.6701409888137293, 0.44102125812106296, 0.3720189777829266, -0.07141894263261345, -0.1774128418528722, -0.30354208357008805, -0.4275334089761112, -0.0843405296795893, -0.33305992609279267, -0.18634741756427506, 0.1819649259117054, 0.07823222358037324, 0.5761213722566578, 0.6443806731518245, 1.2271549476426467, 1.22633775859682, 1.6245890908214324, 2.1090986424002054, 2.6225411973833985, 2.8732681967413805, 3.174471621574694, 3.6470000001830676, 3.9977398078803668, 4.078528978326857, 4.132356858552004, 4.286734453826666, 4.2819319725858165, 4.450665766697424, 4.255794590903461, 4.402758407389919, 4.431317657671973, 4.145127192092033, 3.834710932433604, 3.715456843947146, 3.586852557124275, 3.333358290536751, 2.825797249322493, 2.734350456464978, 2.4406590484485853, 2.334528951246316, 2.121429262277578, 1.713712288008419, 1.8274206184737325, 1.8417608013895261, 1.6891207118402447, 1.9536214229369024, 1.8582079367323698, 2.0962856500212523, 2.5230449177188037, 2.556043127834747, 2.9942635729922724, 3.0840352000955593, 3.8123951325804106, 3.932640267915023, 4.336629783630885, 4.705232356874553, 5.095713769163632, 5.209600158632276, 5.658626507071597, 5.854995933002807, 5.971735631020348, 6.412582235267308, 6.401802875441218, 6.532460973759783, 6.544999903275502, 6.461057531257667, 6.610889268412676, 6.2828322256521325, 5.966575860166805, 5.884242749512557, 5.750676382687146, 5.513052008398646, 5.101432944593169, 5.053819334016194, 4.544391753399884, 4.612686292225666, 4.169841351470367, 4.115932208803083, 4.1214712898081745, 3.9483551366047105, 3.9883052281103346, 3.990119493671286, 4.130896680496937, 4.234402717521389, 4.258601347102199, 4.589481390055532, 4.637345510501965, 5.083599780334533, 5.499143313534273, 5.779208330446664, 6.17114945905641, 6.469109353560552, 6.961644104337085, 7.1845703158191325, 7.467160232512964, 7.892976328396851, 7.9171479965642595, 8.318105647487394, 8.326621174787727, 8.6709701306993, 8.57720553763321, 8.624661417622915, 8.609420503049154, 8.284243078396514, 8.277545726284762, 8.274263396722219, 8.052577437937488, 7.766173471019906 ]
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The given time series is a square wave. What is the most likely period of the square wave?
[ "53.25", "36.57", "19.51" ]
19.51
multiple-choice
23
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Square Wave", "Period" ]
Check the time interval between two peaks.
Pattern Recognition
Cycle Recognition
113
[ -0.022241277845556566, 1.4318231911230748, 1.5350600239656218, 1.6098458042267763, 1.423793075904333, 1.7125327493412428, 1.6363079078724527, 1.5258623371532858, 1.4754318609552945, 1.4796069458851544, -1.5390984326164032, -1.569459314269454, -1.4514687784681561, -1.5808166368266816, -1.4959688729352696, -1.5494115424139057, -1.5195521182567115, -1.5695246975560568, -1.4757474998714566, -1.474265045967411, 1.5925119565786332, 1.6072704867113692, 1.4520821762421134, 1.5788436302873166, 1.671439230745172, 1.5956595106983684, 1.4434473037162663, 1.5415424030315101, 1.5840168478303895, 1.4707791023065877, -1.682346173668754, -1.5781288408400713, -1.6206022947931888, -1.5479479571354156, -1.7056595187460475, -1.4900489477428032, -1.6099795575257543, -1.446943830328283, -1.5742089379760564, -1.5490579586348674, 1.5292155661267268, 1.4777030849863915, 1.7937470002236846, 1.4969324361956902, 1.6488712062429847, 1.4371561593900792, 1.5837321931057347, 1.5944062703896023, 1.5441013562866057, -1.603619525960376, -1.572851244257815, -1.5868480041937307, -1.306316425812873, -1.5487793629409006, -1.4940301761300236, -1.368356544953882, -1.6341509207938367, -1.5427530306796249, -1.5734376671645576, 1.5379052094480823, 1.52861785728467, 1.473331345433397, 1.566060082496082, 1.4402557762548085, 1.6454073277273205, 1.608850034661879, 1.497172772360017, 1.5020207218590327, 1.574070110231244, -1.626373585557037, -1.3653210415829315, -1.5107264811272418, -1.5071927388580508, -1.47837454596069, -1.6044653814034258, -1.4376180558179412, -1.5350657173748723, -1.4515045068965355, -1.5663143666532517, 1.6782468766695373, 1.5866534166988977, 1.7647697564141274, 1.5095668923141956, 1.5669739936054932, 1.4433614293094028, 1.4935892302417966, 1.6083877087812093, 1.6594514977503687, -1.2500180188243464, -1.5561819446587408, -1.6398403947609785, -1.4711864779586945, -1.6593017760465492, -1.490091315193745, -1.596744625615831, -1.5253595569583793, -1.4374144437799519, -1.5575923969044883, 1.6398069624292808, 1.6338349088583335, 1.360085901165509, 1.4563589793928613, 1.3903383678568715, 1.746692764249432, 1.535979861452686, 1.7246019752930828, 1.3832238259422203, 1.489242211541566, -1.4664692298198214, -1.5285915935537502, -1.4932330647150729, -1.6718998773757443, -1.4088387047085258, -1.4688174444206896, -1.5063565034472712, -1.6150800821045994, -1.5141390412358242, -1.5756680518957678, 1.3814699329926425, 1.5414767688242699, 1.530846014174166, 1.6527592049839306, 1.3230764097649983, 1.5700390368818888, 1.430962249185847, 1.5784413882159734, 1.5839470595541492, -1.555203531709438 ]
null
Does the trend of the time series change direction?
[ "No", "Yes" ]
No
binary
12
medium
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend" ]
Check if the overall direction of the time series changes at any point.
Pattern Recognition
Trend Recognition
114
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null
The given time series is a white noise process. What is the most likely noise level?
[ "1.21", "7.12", "3.61" ]
3.61
multiple_choice
51
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Gaussian White Noise" ]
The noise level refers to the standard deviation of the noise. You should check the degree of variation of the time series over time. You can estimate the standard deviation by observing the average distance between the data points and the mean.
Noise Understanding
White Noise Recognition
115
[ -2.390049552706152, -1.9721636300096645, 1.2126595253701191, 3.9945582747949846, -6.077851936581496, 0.321179472209318, 7.308547039129982, -3.910631674747931, -0.6755380067237313, -2.3808632001560333, 2.7702695607700445, -4.546265726538469, -5.594947455222655, 3.089532350694066, 0.7581768021808828, -4.3486313281696445, -0.2076069338561297, -4.786004077978066, 3.323370768762647, 0.7659691900870016, -2.5811330555611693, -1.9586380650794717, -1.659355834484446, -4.030447274870211, 4.78621965812567, 4.264069539648047, -1.4331260519962499, -1.091159814440136, -4.074490089459171, -0.6569050744931306, 1.7802385145438473, 4.2745689605116475, -3.135095836782359, -6.134132910828534, -3.6340571059712095, 1.9951403023507424, 0.07019327492230196, 3.5102845344660056, 0.11180108830991828, 5.741972669486986, -5.598768218373335, -8.86021669033669, 4.8676453208744235, -2.7567020419481243, 0.5299402033519767, 0.5557675275664454, 1.081340455285364, -1.695476053165163, -3.2519564813693576, -3.580515312699705, 2.2616306896132503, 2.8826120900396486, -0.6603000247365091, -2.077721948092354, 1.9884995377060517, -5.23195651684892, 7.037033541460264, 1.683358738938414, -0.5643507531786, 2.9276423238540144, 2.309504251770396, 0.9459638575016887, -6.755815903781764, 1.5327989444597379, 5.240204083754195, -2.1719605784455402, 3.4589601501900384, 3.1230831495387394, 0.8466395256861579, 0.6081230452306997, 2.013298815295626, 1.4109778099663035, 6.907919071347405, 3.587407433160205, 2.736285319771468, 3.8169312178689645, 2.686499215138139, -7.600135668332801, -0.45896309869835095, -2.1706210781558424, 1.1193848620039857, -2.027672949895354, 2.21972779593985, -1.522149142385979, 6.041944195542133, 5.90292143834578, -5.254552750998409, 1.225323472412198, -1.7013114379906527, 0.6656506653602259, 2.198398084268595, -4.55572004204919, -3.6242928730526853, -3.1814554091965097, -1.857221804008745, -1.9110213181356854, 2.9541545742408837, 0.7344877718230297, -3.082048062788305, -1.2471278809652264, -8.519644304426969, 7.333130894690967, -0.14117399218751853, 3.59877727990253, 2.7939492449904586, -0.021016062576876354, -8.58257381995571, -2.040589724863594, 1.4295322698563413, 0.028304628595788195, 3.9517124457341293, 0.18045853603094322, 6.616606779249158, -3.739767585751048, 1.9189688295832548, -3.9661651952892796, 3.291524469764186, -2.8475412350520757, -6.4823230317936495, -5.3988625387613585, -1.7308053365015394, 5.75309977684089, 2.6034747055083294, 1.5751615142964015, -0.09835561976935259, -4.0878672391263535, -3.4216388652188825, -1.9078503439429837 ]
null
Are the given two time series likely to have the same underlying distribution?
[ "Yes, they have the same underlying distribution", "No, they have different underlying distribution" ]
No, they have different underlying distribution
binary
91
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Gaussian White Noise", "Red Noise" ]
You should focus on the underlying distribution of the time series. You can start from analyzing whether both time series are stationary. Then, you can check if they have the same mean and degree of variation from mean.
Similarity Analysis
Distributional
116
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The given time series has sine wave pattern. How does its amplitude change from the beginning to the end?
[ "Decrease", "Increase", "Remain the same" ]
Decrease
multiple-choice
17
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Amplitude" ]
Base on the definition of amplitude, check if the distance between the peak and the baseline changes.
Pattern Recognition
Cycle Recognition
117
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null
You are given two time series following similar pattern. Both of them have an anomaly. Do they have the same type of anomaly?
[ "Yes, Time series 1 and time series 2 both have flip anomaly", "No. They have different types of anomalies" ]
Yes, Time series 1 and time series 2 both have flip anomaly
binary
76
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Cutoff Anomaly", "Flip Anomaly", "Spike Anomaly" ]
For each time series, identify the type of anomaly based on the given definitions. Then, check if they have the same type of anomaly.
Anolmaly Detection
General Anomaly Detection
118
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The given time series is a square wave. What is the most likely period of the square wave?
[ "39.11", "12.92", "59.46" ]
59.46
multiple-choice
23
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Square Wave", "Period" ]
Check the time interval between two peaks.
Pattern Recognition
Cycle Recognition
119
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null
The following time series has two types of anomalies appearing at different time points. What are the likely types of anomalies?
[ "speedup and cutoff", "speedup and flip", "cutoff and flip" ]
speedup and flip
multiple_choice
68
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Cutoff Anomaly", "Flip Anomaly", "Speed Up/Down Anomaly" ]
You should first identify the two places where the anomalies appear. Then, you should check the type of anomaly based on the given definitions.
Anolmaly Detection
General Anomaly Detection
120
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null
The given time series has an increasing trend, is it a linear trend or log trend?
[ "Log", "Linear" ]
Log
multiple_choice
7
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend", "Log Trend" ]
Check if the slope of the time series is constant or changes over time.
Pattern Recognition
Trend Recognition
121
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null
The given time series has a cycle component and a trend component. Is it an additive or multiplicative model?
[ "Additive", "Multiplicative" ]
Additive
multiple_choice
11
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend", "Sine Wave", "Additive Composition", "Multiplicative Composition" ]
For a multiplicative composition, the amplitude of the cyclic component will increase or decrease depending on the trend component.
Pattern Recognition
Trend Recognition
122
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null
You are given two time series following similar pattern. One has an anomaly and the other does not. Which time series has the anomaly, and what is the likely type of anomaly?
[ "Time series 1 with speed up/down anomaly", "Time series 1 with flip anomaly", "Time series 2 with cutoff anomaly" ]
Time series 1 with speed up/down anomaly
multiple_choice
73
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Linear Trend", "Speed Up/Down Anomaly", "Cutoff Anomaly", "Flip Anomaly" ]
You should first identify the time series with the anomaly. Remember, both time series share similar pattern. Then, you should check the type of anomaly based on the given definitions.
Anolmaly Detection
General Anomaly Detection
123
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One type of noise in time series is random walk. Is the given time series noisy based on your understanding of random walk
[ "No", "Yes" ]
No
binary
56
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Red Noise" ]
When we say a time series is noisy, it typically refers to there are random fluctuations that disrupt the overal pattern of the time series. When the time series has a random walk noise applied to it, it seems like the pattern are even more disrupted. Can you check if it is the case for the given time series?
Noise Understanding
Signal to Noise Ratio Understanding
124
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null
The given time series is a sine wave followed by a square wave patterns with different amplitude. How does the amplitude vary over time?
[ "Decrease", "Remain the same", "Increase" ]
Remain the same
multiple-choice
20
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Square Wave", "Amplitude" ]
Focus on the amplitude instead of cyclic pattern change, check if the distance between the peak and the baseline changes.
Pattern Recognition
Cycle Recognition
125
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null
Which additive combination of patterns best describes the time series?
[ "SineWave + SquareWave", "SineWave + SawtoothWave", "SawtoothWave + SquareWave" ]
SawtoothWave + SquareWave
multiple-choice
17
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Square Wave", "Sawtooth Wave", "Additive Composition" ]
Imagine the shape of the time series as addition of two different patterns.
Pattern Recognition
Cycle Recognition
126
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null
Does the given two time series have similar pattern?
[ "No, they have different shape", "Yes, they have similar shape" ]
No, they have different shape
binary
78
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Square Wave" ]
Pattern refers to the general shape of the time series. In this case, you see both time series have cyclic patterns. Do their behaviors at peak and trough look similar?
Similarity Analysis
Shape
127
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Is the noise in the time series more likely to be additive or multiplicative to the signal?
[ "Additive", "Multiplicative" ]
Multiplicative
binary
59
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Additive Composition", "Multiplicative Composition", "Gaussian White Noise" ]
Additive noise is added to the signal, while multiplicative noise is multiplied to the signal. When a cyclic component is added with a white noise, the cyclic pattern still remains. When a cyclic component is multiplied with a white noise, the noise is amplified. Can you check if it is the case for the given time series?
Noise Understanding
Signal to Noise Ratio Understanding
128
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null
You are seeing two time series that are random walk. Are they likely to have the same variance?
[ "Yes, they have the same variance", "No, time series 1 has higher variance", "No, time series 2 has higher variance" ]
No, time series 2 has higher variance
multiple_choice
95
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Red Noise", "Variance" ]
Random walk is a time series model where the next value is a random walk from the previous value. Variance refers to the distance of the values from the previous steps. At a high level, you should check the distance of the values from the previous steps for both time series.
Similarity Analysis
Distributional
129
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Is the noise in the time series more likely to be additive or multiplicative to the signal?
[ "Multiplicative", "Additive" ]
Multiplicative
binary
58
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Additive Composition", "Multiplicative Composition", "Gaussian White Noise" ]
Additive noise is added to the signal, while multiplicative noise is multiplied to the signal. When a trend component is added with a white noise, the general trend still remains. When a trend component is multiplied with a white noise, the noise is amplified. Can you check if it is the case for the given time series?
Noise Understanding
Signal to Noise Ratio Understanding
130
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null
You are seeing two time series that are random walk. Are they likely to have the same variance?
[ "Yes, they have the same variance", "No, time series 2 has higher variance", "No, time series 1 has higher variance" ]
No, time series 2 has higher variance
multiple_choice
95
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Red Noise", "Variance" ]
Random walk is a time series model where the next value is a random walk from the previous value. Variance refers to the distance of the values from the previous steps. At a high level, you should check the distance of the values from the previous steps for both time series.
Similarity Analysis
Distributional
131
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How does the noise in the given time series influence the detection of periodic pattern in the time series?
[ "Distort the pattern", "No influence" ]
No influence
binary
58
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Gaussian White Noise", "Sine Wave", "Additive Composition" ]
When the noise level is high, it can distort the pattern in the time series. Can you check if you can still detect the cyclic pattern in the time series?
Noise Understanding
Signal to Noise Ratio Understanding
132
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null
The given time series is a white noise process. What is the most likely noise level?
[ "6.23", "1.92", "4.16" ]
1.92
multiple_choice
52
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Gaussian White Noise" ]
The noise level refers to the standard deviation of the noise. You should check the degree of variation of the time series over time. You can estimate the standard deviation by observing the average distance between the data points and the mean.
Noise Understanding
White Noise Recognition
133
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null
Is the given time series likely to have an anomaly?
[ "No", "Yes, it's pattern is distorted by random spikes", "Yes, it's pattern is flipped at certain point in time" ]
No
binary
64
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Flip Anomaly", "Spike Anomaly" ]
Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions.
Anolmaly Detection
General Anomaly Detection
134
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null
Are there any granger causality between the two time series?
[ "No, they are not granger causality", "Yes, time series 1 granger causes time series 2", "Yes, time series 2 granger causes time series 1" ]
Yes, time series 2 granger causes time series 1
binary
105
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Granger Causality" ]
Granger causality is a statistical concept that determines whether one time series can predict another. While you cannot perform the statistical test, you can check if one time series can predict the other by shifting the time series by a certain number of steps. Do they look simiar after the shift?
Causality Analysis
Granger Causality
135
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Weak stationarity requires the mean, variance to be constant over time. Does the following time-series exhibit weak stationarity?
[ "No, the variance is different overtime", "No, the mean is different overtime", "Yes" ]
No, the mean is different overtime
multiple_choice
34
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Stationarity" ]
For mean, check if the average value changes over time. For variance, check if the degree of variation changes over time.
Pattern Recognition
Stationarity Detection
136
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null
Is the given time series likely to be stationary after removing the cycle component?
[ "Yes", "No" ]
Yes
binary
36
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Stationarity", "Sine Wave", "Square Wave" ]
Cycle component brings the cyclic pattern to the time series. Assume this effect is removed, does the time series satisfy the stationarity condition?
Pattern Recognition
Stationarity Detection
137
[ 0.40234374207731094, 0.04271175547438197, -0.7206971974503292, 0.765367382542935, 1.068954730943996, 1.2258588712281748, 1.745855740250192, 0.8093399029555053, 1.6779875516169056, 1.9466813222034067, 0.5962232166672544, 1.5066971265430085, 1.05388183439204, 1.1310662176057455, 1.1789071828213742, 0.6250284199259006, 0.34100161332502116, 0.4466014482938373, -0.2904898732278205, -0.5238136008044638, 0.04387848242195863, -1.011277382446841, -0.4000742592351732, -0.6853000393128708, -1.846128364771202, -1.5092174567209873, -0.7655040441543363, -1.8215611470088737, -1.618277871740408, -1.1901913208262633, -0.9979071643978716, -1.3834653242577457, -1.421358444526184, -0.35414362335510596, -0.03202128451737707, -0.17558655144919716, -0.36257320910396773, -0.5384794155512344, -0.3143129991593351, 0.5319688571181093, 0.7090785462611423, 1.365093871480682, 1.2141347518276708, 0.40155022247263006, 1.6950712199006168, 0.8657910141026202, 1.2086255335216132, 1.52231820050731, 2.3011879521215803, 2.755088854742465, 1.5307200707622854, 1.0488632569607101, 1.2196382631259866, 1.198000416290637, 0.44598734143467245, 1.185664472292603, 0.2451470803947754, 0.1322061535597337, -0.023280566823056403, -0.3569089113676803, -0.37519180929799545, -1.2225180137795664, -1.3564143437911391, -1.7323547221803077, -1.1298867869654203, -1.035137568532149, -1.2016647595183492, -0.6315327339060068, -1.337077830315302, -0.9376014184775647, -1.6083542931765298, -1.130028794104345, -0.45380238608779894, -0.17777573216256148, -0.4744040531886303, -0.14215664726520127, -0.5638685700869611, 0.19371017587941014, 0.980722118412313, 1.0428964932919427, 1.1926079150403088, 1.1448762633831415, 1.2512739144275926, 1.0397583759429665, 1.710547566609237, 1.6001476988747219, 1.609048551686253, 1.0855345429491647, 0.2489972172484175, 1.5914046150571144, 0.7139833443622674, 0.3515864720860795, 0.8488210092962649, 0.1328278357217662, 0.5593715385762462, 0.12411548692796329, -0.07569484724953457, -1.224813787694548, -0.25792389221053447, -0.8694652889602723, -1.3084306863323634, -1.1972167117241368, -2.154513222632752, -1.2124555347913868, -1.8752938123652134, -1.9934702064812144, -2.0037908523004297, -0.7053944102303591, -1.3591830963836964, -0.2117607610722938, -0.9593508490180317, -1.27975411746021, -0.7866596455758007, 0.5133042771131965, 0.11606039286993582, 0.16699958607101173, 0.7057776492065991, 1.0607558789976992, 1.5169229388550631, 1.8160777811792759, 1.1126309972664714, 0.7558662677395565, 1.2139122688204262, 1.5248393237414108, 0.9359059791304297, 0.8672818380845962, 1.3498386168397287, 1.0311544191374804 ]
null
What type of trend does the time series exhibit in the latter half?
[ "Linear", "No trend", "Exponential" ]
Exponential
multiple_choice
14
medium
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend", "Exponential Trend" ]
Focus on the pattern of growth or decline in the second half of the time series.
Pattern Recognition
Trend Recognition
138
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null
In which part of the time series does the anomaly occur?
[ "Middle", "Beginning", "End" ]
Middle
multiple_choice
78
medium
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Linear Trend", "Spike Anomaly", "Cutoff Anomaly", "Wander Anomaly" ]
Identify where in the time series sequence the unusual pattern or disruption occurs.
Anolmaly Detection
General Anomaly Detection
139
[ 0.04967141530112327, 0.4330713223475383, 0.9431119684560556, 1.431724304507175, 1.6128588001157182, 1.9131683458997304, 2.327914105028443, 2.405225246590738, 2.3596834391546477, 2.4560733253620146, 2.2679629727003014, 2.1006610845344142, 1.9307104222875915, 1.4092884604991975, 1.0677881638394338, 0.7819088926955307, 0.3069940469892613, -0.0028248244141300324, -0.5647522710232745, -1.0366598245495968, -1.1373630883618082, -1.6484101450129631, -1.9023985083288835, -2.266405777085652, -2.317040856919618, -2.3091530929890705, -2.4098569358413497, -2.1493600723709907, -2.060481507653071, -1.7707832525473686, -1.4795974392440296, -0.8597419542577167, -0.6325246016473576, -0.2981138594081842, 0.33860928752967134, 0.5773184346851206, 1.142369154022031, 1.3120388424765996, 1.712801553715744, 2.1423616503168903, 2.4034754166362684, 2.476512489172009, 2.4959333902867344, 2.442307936325379, 2.2076003956473684, 2.088782322469231, 1.8491665874679126, 1.6738907358986845, 1.2254477386244877, 0.6008646826502724, 0.3733325292318854, -0.14088194488592648, -0.6048975788641335, -0.8871783116516577, -1.2182948019883162, -1.5501484559130798, -1.9866176973263607, -2.1214524854987293, -2.1670246662383437, -2.130090640033191, -2.2198783558219, -2.053505456967347, -1.931885642193999, -1.6578578412027294, -1.114392627169214, -0.6696755967161876, -0.3878861784469947, 0.1638742755792365, 0.5481237155758325, 0.8846142219941437, 1.3960449953206069, 1.8839057338714178, 2.0433497307493713, 2.455933516275172, 2.2170478686077226, 2.6616313831142966, 2.60603249377154, 2.502255455111315, 2.3954660473164378, 1.9661434045969632, 1.853818778004168, 1.5648133260107473, 1.2847960655199706, 0.6614107464877258, 0.1917860454710683, -0.21955044248669758, -0.5057777832851453, -0.963334003660819, -1.4050496545090916, -1.6011073374932, -1.8770363355729942, -1.9498908398635244, -2.1970098282282384, -2.156776593780896, -2.0775847850051314, -2.019130099860886, -1.6032775196925309, -1.3008513725358175, -0.9650172210446712, -0.5845419830135729, -0.26913345895424845, 0.2778817590561014, 0.731798478323979, 1.115121347097851, 1.5768157521065314, 1.9855237155348084, 2.4283338303811033, 2.4840384368195734, 2.643672932876809, 2.6811131728179385, 2.4842615938142703, 0.01706604953678462, 0.008735894156353792, 0.00009144345740137356, -0.0036553929683254232, 0.006490867326291062, -0.012228735366753068, 0.005363360323820653, -0.009146909312612774, 0.0062054821596173515, -0.0016093737686306336, -0.0038826439897661413, -0.008855123738288487, -0.0035674502576819415, 0.005561217985339653, 0.01043860610918658, 0.005264481613346957, 0.013638865244171053 ]
null
In which part of the time series does the anomaly occur?
[ "Middle", "End", "Beginning" ]
Middle
multiple_choice
77
medium
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Linear Trend", "Spike Anomaly", "Cutoff Anomaly", "Wander Anomaly" ]
Identify where in the time series sequence the unusual pattern or disruption occurs.
Anolmaly Detection
General Anomaly Detection
140
[ 0.04967141530112327, 0.3936274030656152, 0.849182714639081, 1.2549918355909608, 1.3151035090382572, 1.4509555824018325, 1.6581762625732248, 1.4912376481380742, 1.1768880160617987, 0.9972042085842798, 0.5469731523277402, 0.15503610885493446, -0.17812272508370677, -0.7790239641330681, -1.0976411940037913, -1.2451543327072274, -1.460117110620757, -1.390347394277366, -1.4635171095470785, -1.3563235577582289, -0.8139717951801537, -0.6505713882165962, -0.23554894359683204, 0.025213912049279863, 0.5168541059614495, 0.9493983582443769, 1.126190343345994, 1.4952076769373583, 1.5112232429352968, 1.5447592744039151, 1.4054652019061389, 1.4400865445493471, 0.9564360326465227, 0.49135053092576836, 0.28262723488206687, -0.3243140968747241, -0.5591262877101522, -1.1001439721196793, -1.2827909465712342, -1.2788799418605787, -1.2646183957437804, -1.2492468882111207, -1.0990568715900189, -0.8451355914288413, -0.6171364523150624, -0.14808745531343226, 0.28903847013822553, 0.839267031311977, 1.1238020625514662, 1.1998547065108427, 1.604741533096107, 1.6249150493714912, 1.575131610033124, 1.573530655713472, 1.3852647476530195, 1.0630121219966895, 0.5154522193533473, 0.16796302130792018, -0.16794506320362243, -0.4724652150771129, -0.9277461825552449, -1.125398029579993, -1.3442495925273594, -1.3699550780312468, -1.0741992819693464, -0.8202361368411266, -0.6735584922196524, -0.20817796482394674, 0.12703569033831213, 0.4374546549539383, 0.9301232199333345, 1.3913986514883327, 1.5035449435542572, 1.8389662466685355, 1.4887738908333672, 0.01706604953678462, 0.008735894156353792, 0.00009144345740137356, -0.0036553929683254232, 0.006490867326291062, -0.012228735366753068, 0.005363360323820653, -0.009146909312612774, 0.0062054821596173515, -0.0016093737686306336, -0.0038826439897661413, -0.008855123738288487, -0.0035674502576819415, 0.005561217985339653, 0.01043860610918658, 0.005264481613346957, 0.013638865244171053, 0.025391627162693324, -0.003244909578288935, -0.0020586671709550515, 1.487689002867509, 1.8176681118735631, 1.859354285620509, 1.7669902258950627, 1.5696023344869556, 1.1848257207349728, 0.9447306826151886, 0.5660002770877876, 0.11602364844623353, -0.2106141975044671, -0.5017569912874696, -0.6316336684363655, -0.9900270974641897, -1.0636942167478254, -1.067128053896584, -1.1120393836096476, -0.6837993843010337, -0.3543926603669414, 0.26441338979671813, 0.40682260396574854, 0.8631148736169125, 1.2048702238831175, 1.4072626599759726, 1.8710373665434228, 1.9641160321065092, 1.9900295115272857, 1.7304384601295595, 1.7675894733618407, 1.2034664752968072, 1.05077928333778, 0.818500394457654, 0.09660252172745207, -0.2451687516521757 ]
null
The given time series has a decreasing trend, is it a linear trend or log trend?
[ "Linear", "Log" ]
Log
multiple_choice
8
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend", "Log Trend" ]
Check if the slope of the time series is constant or changes over time.
Pattern Recognition
Trend Recognition
141
[ -0.0237391961206669, -0.08488089941461782, -0.4876906952945991, -0.7549578951558598, -0.9407691843274766, -0.9518735405251835, -1.2559534913594663, -1.2896820087844902, -1.0988993937682607, -1.2064164791869096, -1.4303772786154836, -1.5371382661014765, -1.5527835584779865, -1.3875586312989792, -1.6417660385118686, -1.6478083229572325, -1.8125222582540657, -1.8369642881690516, -1.8266608914719449, -1.924863002981436, -2.1712740276340257, -1.8956861430238954, -2.125748203047924, -1.892021493621596, -2.0719287542474834, -2.169623956268892, -2.167637625575894, -2.256095594336458, -2.326467359021412, -2.2886145896813535, -2.2514606491030142, -2.416090186893398, -2.460683015791672, -2.4620227572640845, -2.358523900132364, -2.3349981358320373, -2.4698943260323, -2.473155812170788, -2.618829770407448, -2.4921673259518506, -2.548090452836211, -2.6971622829717954, -2.827289939594563, -2.7924634505386945, -2.758741717617504, -2.596593713907015, -2.5780369074919007, -2.7973968794297375, -2.8139619047227646, -2.7663517693197006, -2.8102760401779383, -2.7370204831216425, -2.9007154091108474, -2.771436744377467, -2.9079112818449264, -2.815923383501666, -2.9454616640465443, -2.9964184534516027, -2.760352597098294, -2.8612699844500744, -3.1629470090214675, -3.044119248896467, -2.894406337937403, -3.1497861308827377, -2.968045481745195, -2.9931794547370516, -3.1443564412170972, -3.089671345008654, -3.311936319724502, -2.939839006776981, -3.161547159964072, -3.0638256985324968, -3.0962103060059767, -3.0350169394451596, -3.22717369067508, -3.1763315965150984, -3.153509920735807, -3.1347142103749723, -3.1189249727445962, -3.2943389318943535, -3.325115839141038, -3.1346298149222407, -3.4861553342959324, -3.137113526824293, -3.3358835963785807, -3.317510345227365, -3.396562651286988, -3.3442965738908805, -3.2233889140209264, -3.284321895991208, -3.3229273798576897, -3.3739814135825505, -3.537989692975798, -3.488292019671317, -3.448540018034769, -3.378687960757168, -3.4276223424206758, -3.346267552272011, -3.407186008066866, -3.4589468018608147, -3.2465430933556156, -3.4431400154470224, -3.536508256156142, -3.3167783214999824, -3.392106181470961, -3.3398238461758765, -3.5417256836399575, -3.4421175757665954, -3.694067081382911, -3.4793342647326018, -3.5823806305955856, -3.7109093153037414, -3.4808620812535205, -3.7737784985777894, -3.792605196246388, -3.5735832697592436, -3.561120955133178, -3.5295577301573315, -3.5606989982768753, -3.4954465444964447, -3.5812382478969345, -3.672576735736312, -3.6747273197247634, -3.703543944099508, -3.6536668971963975, -3.8007802517860885, -3.664723611761545, -3.7151674387119953 ]
null
The time series has three cyclic pattern composed additively. Which cycle pattern is most dominant in the given time series?
[ "SineWave", "SquareWave", "SawtoothWave" ]
SineWave
multiple-choice
20
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Square Wave", "Sawtooth Wave", "Additive Composition", "Amplitude" ]
The cyclic patterns have different period and amplitude. The dominant pattern is the one that has the highest amplitude. Identify the pattern with the highest peak.
Pattern Recognition
Cycle Recognition
142
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null
You are given two time series where one is the lagged version of the other. What is the most likely lagging step?
[ "Lagging step is between 5 to 20", "Lagging step is between 30 to 45", "Lagging step is between 60 to 75" ]
Lagging step is between 5 to 20
multiple_choice
98
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Lagged Pair" ]
You already know that one time series is the lagged version of the other. Shift the time series by lags proposed in the options and check which one looks the same as the other time series.
Causality Analysis
Granger Causality
143
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The following time series has a noise component. Is it a white noise or random walk?
[ "Random Walk", "White Noise" ]
White Noise
binary
52
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Red Noise", "Gaussian White Noise" ]
White noise is a stationary process with a constant mean and variance. You should check if the time series has a constant mean and variance over time. This can help you distinguish between white noise and random walk.
Noise Understanding
White Noise Recognition
144
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null
Is time series 1 a lagged version of time series 2?
[ "No, time series 2 is a lagged version of time series 1", "Yes", "No, they do not share similar pattern" ]
Yes
multiple_choice
97
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Lagged Pair" ]
Focus on the time delay between the two time series. If time series 1 is a lagged version, then it should look the same to time series 2 after being shifted by a certain number of steps. Can you check this?
Causality Analysis
Granger Causality
145
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Is the two time series lagged version of each other despite amplitude difference?
[ "No, they are not lagged versions", "Yes, they are lagged versions" ]
Yes, they are lagged versions
binary
101
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Lagged Pair" ]
Try to shift one time series by a certain number of steps and check if it looks the same as the other time series despite the scale difference. If they are lagged versions, they should look very similar in general after the shift.
Causality Analysis
Granger Causality
146
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Is the noise in the time series more likely to be additive or multiplicative to the signal?
[ "Additive", "Multiplicative" ]
Additive
binary
57
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Additive Composition", "Multiplicative Composition", "Gaussian White Noise" ]
Additive noise is added to the signal, while multiplicative noise is multiplied to the signal. When a trend component is added with a white noise, the general trend still remains. When a trend component is multiplied with a white noise, the noise is amplified. Can you check if it is the case for the given time series?
Noise Understanding
Signal to Noise Ratio Understanding
147
[ 0.02861030172655463, 0.05633793010038873, 0.1515727469049879, 0.059273822409525834, 0.1188204942765912, 0.3285419867119834, 0.43317603749392575, 0.5004956839353788, 0.33658539339337007, 0.4458272089074648, 0.34974031844312237, 0.669460038203733, 0.3771781522699744, 0.6526042995203668, 0.8281131413395282, 0.7274236087054043, 0.8084157757198273, 0.7430785425155875, 0.48023599086931557, 0.7582761196534178, 0.9457559361313738, 0.8489560057515326, 1.1258127688687913, 0.7908706250059645, 0.744985826289513, 1.307472135583692, 1.157147358879329, 1.3328870008110665, 1.209840489493335, 1.4693640796275975, 1.671478843122261, 1.7322283277110577, 1.3723929054835207, 1.463520749582236, 1.6263410032543977, 2.0063245541818477, 1.4609187489546742, 1.5091544674332977, 1.950629208224572, 1.6968791019500669, 2.0032447440712673, 1.8064030778326063, 2.036844124952518, 1.9148068195455912, 1.8647622001924167, 2.1370599368718493, 1.917758044626726, 2.2914855928687237, 2.203075530793508, 2.029234381532392, 2.401418207656846, 2.4441464451078216, 1.975655416334137, 2.4732591425209725, 2.5320219528003416, 2.5950891456955487, 2.2675496821331507, 2.7399238033795426, 2.8179833703559924, 2.5806268740420384, 2.537154773421884, 2.6662236701112505, 2.9301349196409245, 2.709313906731151, 2.7334861503354633, 3.456693338185815, 3.0912842565090664, 3.078224984281119, 3.0547066560449463, 3.1500117492030477, 3.632667655582947, 3.48647152602231, 3.840932603801208, 3.4970672388943087, 3.2798519313990067, 3.567118639396334, 3.6683165663694712, 3.727958569813572, 3.567959325483696, 4.054742894934655, 3.4996018691812307, 3.8616134728571625, 3.784707487911052, 3.7366512801506317, 3.98408601624121, 4.228664251721324, 4.108519549280947, 4.129736236593025, 3.727516771025783, 4.3503882701835135, 4.137272089591809, 4.368288117767489, 4.490749504362983, 4.3951453905509315, 4.012252299263496, 4.728526746009823, 4.851512206873742, 4.704042567649025, 4.449340330749418, 4.879372417783514, 4.630092752019087, 4.719075504071049, 5.0136048346556334, 4.952724746663337, 4.411883401221159, 4.960319236259087, 5.198633092900776, 4.746228369683527, 5.251625780798129, 4.779965905201797, 5.1118024511423945, 5.3067772651249125, 5.1980032011017965, 5.525096945319837, 5.064193173243778, 5.547820376790955, 5.445356762221903, 5.3148580246199595, 5.614640204515804, 5.561778277107129, 5.912208358160441, 5.783533143455414, 5.796940425536007, 5.631156927570239, 5.838529645393811, 5.744018104769595, 5.891326523606509, 6.194308017610108 ]
null
The given time series is a sine wave. What is the most likely amplitude of the sine wave?
[ "1.59", "6.7", "15.08" ]
1.59
multiple-choice
21
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Amplitude" ]
Check the distance between the peak and the baseline.
Pattern Recognition
Cycle Recognition
148
[ 0.15622442106382914, 0.461272626002808, 0.9341561163734894, 1.2861784628772857, 1.650152258185737, 1.5285761159638223, 1.5120616025796951, 1.3301000058631183, 1.136894923031432, 0.7456243780305377, 0.1901674415798182, -0.21569129429142275, -0.7715849303298297, -1.1840428724704146, -1.3185291085142419, -1.5245329526830391, -1.6383660754604756, -1.3119980128595665, -1.149069454385005, -0.773533302472898, -0.1949624362729967, 0.1893083278919083, 0.7012285994798241, 1.1314673174857512, 1.291486725269176, 1.5862038351800065, 1.717592177728859, 1.3756853586149318, 1.2690567627596077, 0.7807472833229943, 0.5256553145530707, 0.04539408927578559, -0.5653850366161892, -0.7341777957861285, -1.1814094844811627, -1.4389209785167452, -1.5226039450651014, -1.4206207473007901, -1.4095668442174527, -0.960484701534581, -0.5210207740165423, -0.12706388889483142, 0.4397623578197221, 0.9604454202943822, 1.1617266183612818, 1.4538460486538276, 1.4977743428746284, 1.3756993152940653, 1.4632279305486997, 1.085690716125122, 0.6953941010231631, 0.13051499043678225, -0.2719795426535605, -0.6029740958618505, -1.1059071687243491, -1.3942315977322692, -1.5384589550164605, -1.7008381761731943, -1.3869603324554953, -1.3132929192489713, -0.9103032856432284, -0.4744005605904557, 0.09400151374136234, 0.5469768396294351, 0.9989994641835339, 1.2331757638856367, 1.6265182278093797, 1.6706368535209926, 1.4844552114827796, 1.2298302893545547, 0.8775031669180641, 0.4757603402655287, -0.10874055433925531, -0.5430583219662005, -0.7981545880464237, -1.2093560238097858, -1.316945269374363, -1.6143389037849158, -1.578214530977586, -1.3980078468277486, -1.078880068503621, -0.7059635969815212, -0.14673987871104377, 0.3112641721881038, 0.7110624065158982, 1.2704538605591431, 1.6124512462764193, 1.6854363456303516, 1.6256190773717334, 1.5019590860918168, 1.3356545858767026, 0.8440128675463487, 0.25646760244197925, -0.12788652860614588, -0.5711477465892696, -1.141340743803385, -1.3821886005722708, -1.4303992369254142, -1.7671916531276464, -1.6078472580990857, -1.2613936665834258, -0.854408823381175, -0.589347070573609, 0.011842066767011388, 0.6157368855127989, 0.9368301606319743, 1.3675486580020506, 1.4404167766088627, 1.5060569175292156, 1.6127106058429157, 1.364351119616435, 0.8858101844891955, 0.5084255599902553, 0.2747284217385211, -0.4553529403869533, -0.7170999658247657, -1.23109241901982, -1.4469958824590647, -1.5469482250936277, -1.770572259693043, -1.5466222512343946, -1.1021989976616144, -0.7911021807042133, -0.16044604611172714, 0.06188502116203132, 0.7410648158292896, 0.9457068768414966, 1.3468068794561634 ]
null
The given time series has square wave pattern. How does its period change from the beginning to the end?
[ "Remain the same", "Decrease", "Increase" ]
Increase
multiple-choice
19
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Square Wave", "Period" ]
Base on the definition of period, check if the time interval between two peaks remains the same.
Pattern Recognition
Cycle Recognition
149
[ -0.0920582894005879, 2.8575567196808236, 2.8683262053468837, 2.935668973854028, 2.81768406396, 2.9387690263559376, -2.858305629611867, -2.9535879770474165, -2.987278216006028, -2.898297855566191, -2.9270975050652344, -2.8847705758368356, 3.063792402390187, 2.9136694636069613, 2.8428827026361767, 2.817716797569984, 2.8215855956320315, -3.053082389475302, -2.813531397929632, -3.1301336406997664, -2.8579952769922596, -2.993612149431713, -2.958046280226602, 2.817006994609751, 3.051392224728393, 2.9084373189327675, 2.9858436112684754, 3.0368584446237907, 2.9231463061664305, -3.026368135849711, -2.9288700337757674, -2.9810305601208102, -2.95715617495292, -2.916268138029119, 2.8933901945549634, 2.9480166748662184, 2.9625361881422387, 2.8933912411375444, 2.947152650574982, 2.851869773270646, -2.7753277762045396, -2.9633568875859546, -2.7870871103019472, -3.0544124679413622, -2.9448464845052746, 2.8737079414028295, 2.9347049382390376, 2.9929874390860722, 2.781866238458237, 2.7960074545598994, 2.7666149519925662, -2.774787339044348, -2.8770452644844777, -3.0152428796099024, -2.951115246159797, -2.7886296191980406, -2.934617681795314, 2.8730869449850727, 2.828676088081884, 2.804306527382314, 3.0251381398085058, 3.074195917638396, -3.005325199042925, -2.766311525842378, -2.9555447003932005, -1.2581404040907405, -1.1704838231461883, -1.3260128631979924, -1.344299395654365, -1.2313971072488106, -1.4225004873312541, -1.196799065843586, -1.297224264383371, -1.369280254443047, -1.3719219639592146, -1.4498514548104657, -1.365122420290028, -1.3379089798270691, -1.4176345567987774, -1.3160517012405295, -1.2642134951750796, -1.332852417227571, -1.2452326209757025, -1.3922192019255628, -1.2111669272056351, -1.2993237330501433, -1.1569794866521548, -4.260684582676878, -4.54913067313108, -4.545012701590897, -4.526730526095623, -4.4425360888832, -4.369509316853242, -4.567387162317732, -4.4639818872529435, -4.515699042886328, -4.440295403646692, -4.351160544559982, -4.526139137644052, -4.669093080568124, -4.427716146212722, -4.585376835761297, -4.516417887597129, -4.42340323021359, -4.47236682854764, -4.512278316456055, -4.559694485146583, -4.792095691928251, -4.432274232325131, -4.527401483823327, -1.1601715757920565, -1.2717024006314421, -1.3656177408811134, -1.2048181947900252, -1.19639226303181, -1.3334083850628768, -1.1921407170703064, -1.367036009166234, -1.2955404266868389, -1.254364364855751, -1.2330270452262102, -1.2493530187660402, -1.3447709216839463, -1.202135439558485, -1.3239479359382709, -1.3684079398551485, -1.3669390003589623, -1.1175063644799956 ]
null
Two time series are given. Both of them have a noise component. Do they have the same type of noise?
[ "No, they have different noise", "Yes, they both have Gaussian white noise" ]
No, they have different noise
binary
87
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Gaussian White Noise", "Red Noise", "Additive Composition" ]
When a white noise is added to a time series, it is expected the random fluctuations have similar amplitude or distribution. Random walk, on the other hand, can result in very different noise patterns.
Similarity Analysis
Shape
150
[ 0.7156090679730908, -0.20749847359561122, 0.40116851486898397, 0.17882054468847064, 1.4673504405438473, 2.2784310977767226, 1.769229374147225, 1.9953731464032611, 2.074681000304986, 1.7546745451585584, 0.08827498569657277, 2.9244923756262686, 2.9958013418996017, 0.8980281790294186, 0.6757403693868048, 1.5493559157942844, 1.356752357314897, 0.45448564975154915, -0.7581723471634804, 0.6216839357205857, 0.68429426890806, 0.6725036667474024, 0.11102416184140984, -0.6888646276591696, 0.40636254119798965, -0.6781897709822661, -1.3482781425509374, -1.183044970370064, -2.8443837975841273, -1.3514619464864406, -0.5247455640064721, -0.8895083324299382, 0.3034860813086784, -1.4400953100554943, -0.6227247642704878, -0.2247306137832854, 1.441312832775873, -0.6201540793221895, -1.6549208328423928, 0.4054024689272923, 1.6015681260816792, -0.402276218551246, 0.9476161659869815, 1.5190789562429987, 1.4845667333533432, 0.7594769319673207, 0.08730813395909687, 0.8383255504131505, 1.490916118598827, 2.029548463545663, 1.5185374392360151, 0.840754558518212, 1.7840878853612012, 2.0686298551336404, 1.5402451758976345, 0.24989203913761882, 0.8051825391755183, 1.8122682987216914, -0.6605576495866883, -1.5292917386299698, 1.9004493449770123, 0.7232538632639257, 0.6331772536738159, -1.236368265507581, 0.3956412671608474, -0.24112444741750244, -2.2633319627348745, 0.5982366177108253, -0.795441191367406, -1.4563810497710827, -1.5201166988729387, -3.2129573470710495, -0.6325092696992819, -0.9639556589390816, -1.3917173222328965, 0.13651949919545636, -2.4439355670784595, -1.5506222279063717, -1.6757323681604996, -0.9574361700610741, -0.44850774205590127, 0.08854519125435595, -0.506092792409236, -0.8504729595268861, 1.8982746554498773, 2.8044195056415724, 0.7075600185914164, -0.033967787323529564, 2.3402113404790303, -0.4599086886339122, 0.7515098638554935, 0.4646616097940919, 0.04908898988704413, 0.9486981295276808, 0.3564386349379496, 1.1971302897490814, 1.148324462957931, 0.4107473198788867, 0.5488025758020031, 0.5545978523097033, 1.443391185434518, 0.31866885994215466, 1.114629320806454, 0.6919136160873233, 0.49136848369110225, 1.1232613056554257, -1.5623235616040794, -1.0842947901169142, 0.9980382465782042, -1.159156784409932, -3.832838915165623, -1.313193420669342, -1.0423064344364552, -3.8343523131756374, 0.1046490485851368, -1.9268240264989709, -0.8405223469445482, -1.3242006029283018, -2.290490745781428, -1.9535602383727548, -1.6787742747742747, 0.4984150841710838, -0.5648779365649531, -0.7597067454500235, -0.9874911734729965, 1.3294410195074298, 1.7806049890624567, 1.6769964181944346 ]
[ 2.0716851329757078, 2.000125582404493, 2.107899010680336, 2.209246287616815, 2.4335069948515473, 2.498508284679163, 2.071977876107993, 1.9227463494779333, 1.7871075314906668, 1.3940782975644448, 1.3715589007584819, 1.3729903055607142, 1.0055464501068379, 0.727302242432753, -0.34333600959213684, -0.8029119812760566, -0.7369192942503382, -1.0835836964414642, -1.4575153189684333, -0.7373341025458109, -0.08248030974819187, 0.20550667681770696, 0.7952486126921285, 0.32372025811332883, 0.3809763331630043, 0.5724001637339268, 0.8486253312870063, 1.0246264498395043, 0.4753480964885247, 0.853494537224507, 1.2143254770398386, 1.8481394843658197, 1.595323085674862, 1.2274165449650765, 0.17108026740264015, -0.16012002651188795, -0.15055631047355122, -0.6880705521336261, -0.3244403975465807, -0.7625539713966369, -1.0429020901339392, -1.7854434386043665, -1.9153455629292564, -2.7290406972745638, -2.564808438834219, -2.3024052480789505, -1.7409628925097596, -1.8953388135920193, -1.5861488259740857, -1.0890912632784175, -0.5986509351078058, -0.5326103199067282, -0.9860812075358143, -0.5533696563122883, -0.3372427535210909, -0.32556245541986994, 0.053001308796729596, 0.0751842993440981, -0.4169897432141676, -1.1989357524765631, -1.7890467953894675, -1.264542041993653, -1.345422319080896, -1.6318391516294484, -2.047518070868542, -2.468802136204311, -2.379708737307846, -2.789541175606403, -2.7715293716553453, -2.9816039646877015, -2.338876031792744, -2.0092741130086944, -1.4287088248942723, -1.387769746115405, -0.7393140665271758, -0.5094322253818722, -0.11873053541563243, 0.7798577215399713, 0.758789262852937, 1.110585903491053, 1.479535178225121, 1.8008857907724853, 1.7134057330846826, 2.3866787703567796, 1.842399115214362, 2.139716926133073, 2.287542775268129, 2.612740395460091, 1.7377428855199946, 1.0915057740213086, 1.2449372503810117, 0.3024860413707793, -0.49901120700768287, -0.8598120648754175, -0.9142727511770157, -0.4142732605058477, -0.420847072664548, -0.4842700818154624, -0.16399870749041068, 0.27198855361959395, 0.7441664127909162, 1.0349548850640855, 0.7355144299867276, 0.7656373379683723, 1.539663329902569, 1.8446207297555264, 1.9077038199034613, 2.3708440913415796, 1.9945096189201155, 1.833379187592078, 1.7115136265102933, 1.3240129957280926, 1.060694018871545, 0.6619262411482378, -0.16050615946584865, -0.4507011916412759, -1.2210339897047207, -1.4466477370513664, -1.205787213500941, -1.4430046092704358, -1.8936986529227209, -1.9069644914967558, -2.0698443400011177, -1.6763882192666508, -1.4641201404280286, -0.57924815725503, 0.15464480598441013, 0.4590460567912761 ]
Are the given two time series likely to have the same underlying distribution?
[ "No, they have different underlying distribution", "Yes, they have the same underlying distribution" ]
No, they have different underlying distribution
binary
91
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Gaussian White Noise", "Red Noise" ]
You should focus on the underlying distribution of the time series. You can start from analyzing whether both time series are stationary. Then, you can check if they have the same mean and degree of variation from mean.
Similarity Analysis
Distributional
151
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The given time series has a cycle component and a trend component. Is it an additive or multiplicative model?
[ "Multiplicative", "Additive" ]
Additive
multiple_choice
11
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend", "Sine Wave", "Additive Composition", "Multiplicative Composition" ]
For a multiplicative composition, the amplitude of the cyclic component will increase or decrease depending on the trend component.
Pattern Recognition
Trend Recognition
152
[ -0.05073327753885459, 0.3060269203951952, 0.8251864995397192, 0.8963476547197375, 1.471539499728654, 1.4327901472015316, 2.1592179229778363, 2.0781348953730916, 2.1956887138400476, 2.442370731575983, 2.4729142656035408, 2.6848376435990997, 2.807417500938879, 2.551866302558415, 2.646718381408645, 2.33185540247207, 2.4461231895909377, 2.2068005906514827, 2.063771935711125, 1.7080987956027525, 1.419288283787723, 1.2706784730545058, 0.7741281314393671, 0.4421298717591514, 0.3291018358941048, 0.011591278315217501, -0.15943314765112668, -0.3516977131997373, -0.6289744171754021, -0.7730825416422021, -1.000487958908561, -1.104169255118397, -1.2461492787677582, -1.2265236126390437, -1.2512757886276638, -1.1862159379072232, -1.14243173305861, -0.9093142834554013, -0.8851242552503945, -0.6019859380687873, -0.22074279409681866, -0.16841428728933455, 0.014073477965299452, 0.5909114816675709, 0.8671864884193435, 1.3413147256485793, 1.577209255773836, 1.8764488558701626, 2.2664818972532332, 2.705259602406253, 2.9545283087224994, 2.9286284943308893, 3.4349474181725355, 3.6495447709385216, 3.7955721727610396, 3.8668809088645655, 4.135233221364307, 4.184140554532121, 3.839271919795415, 3.9361251381522404, 3.9156827949698565, 3.8993660619933332, 3.6454686505246494, 3.377889191486667, 3.292586381379388, 2.9518765701015606, 2.6939716839118573, 2.422710183924268, 2.195519939650775, 1.818916758923907, 1.635258007115725, 1.287149049309731, 0.9325278542412344, 0.802664216310227, 0.4993029731660867, 0.4312844900576097, 0.37227409393845906, 0.08310145318488704, 0.1694990734224347, 0.03592644249492394, 0.16876741787761776, 0.30841123936545395, 0.12643638719054046, 0.3864928561429855, 0.6587102843663857, 0.9607076297464252, 1.1782148585924894, 1.48348379765501, 2.0081466366937324, 2.01549433768106, 2.594527252605616, 2.7955810600342947, 3.2344001983760537, 3.734421097723184, 3.919723803413661, 4.198990198665888, 4.608743881750389, 4.819714448140572, 4.852663172666286, 5.150671860660532, 5.098320718480933, 5.322884336775399, 5.471659359721013, 5.399299335851838, 5.462221987242797, 5.366097768357434, 5.312677613587072, 5.069919574423082, 4.836718013567158, 4.866081371488962, 4.430018657175887, 4.285834833747971, 4.088073946427363, 3.675842416117378, 3.285307269997715, 3.0668713491273385, 2.90876191485259, 2.564276364972701, 2.1797625067247908, 2.1417789892198416, 1.9708687865135326, 1.7043269037847957, 1.684492983359603, 1.572056398552713, 1.4944080220079599, 1.3951277063845922, 1.686729434096716, 1.7529767071791449 ]
null
What is the most likely mean of the given time series?
[ "25.33", "1.93", "-15.7" ]
1.93
multiple_choice
41
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Mean" ]
The given time series is stationary. Check the average value of the time series over time.
Pattern Recognition
First Two Moment Recognition
153
[ 1.94399224370858, 1.916031987535516, 1.9263852600445448, 1.9010717301925142, 1.923716990610491, 1.9105710237261553, 1.9272978797722116, 1.9150265142941176, 1.9253657474018004, 1.9086368344108342, 1.9469998633483407, 1.92234629282268, 1.9288925188060242, 1.9258135220096666, 1.9287653156252182, 1.9286681197025297, 1.9441539396041536, 1.9231086730699742, 1.9147437091138362, 1.9294303650356681, 1.916805204228484, 1.927181030561597, 1.9083361635016574, 1.9488722091426787, 1.9135722767773378, 1.9517578416888421, 1.9274469466534963, 1.9451378247578972, 1.9138272563967593, 1.8906927507183156, 1.8968264599332358, 1.9768974542048543, 1.917093802133647, 1.9497666207614595, 1.9035969701882365, 1.9320752715920106, 1.935345304797813, 1.9833296079699014, 1.9224955274392004, 1.914908133850162, 1.935394029435425, 1.910790623040197, 1.9365250388822313, 1.9275942410348739, 1.914087511169904, 1.9381606494509573, 1.930714947100375, 1.9432895695905468, 1.9604954758374065, 1.9189510151792766, 1.9206893827508007, 1.902673329430283, 1.9386745576627664, 1.916190988283946, 1.935289775132397, 1.951103450798887, 1.91542634818088, 1.9194786924557448, 1.9481919865401884, 1.9422693727020526, 1.9402262008812001, 1.9210774449855976, 1.900929423306543, 1.9148972211192734, 1.9091567311970379, 1.9398416856329443, 1.9341131890443943, 1.917080752533737, 1.92037371521748, 1.9377993353853356, 1.9224671304539427, 1.9174176876378082, 1.9667854528350996, 1.9180256389843784, 1.9415213907744526, 1.9361971491074317, 1.8992479257875694, 1.9155305817457886, 1.944423626029679, 1.9260900222126678, 1.900372630026436, 1.9362340344345865, 1.917395355373479, 1.9370325622892708, 1.980462949570683, 1.9360016518878538, 1.940432360123686, 1.9237272290859329, 1.907033176396801, 1.9483735137050024, 1.9169414602112078, 1.9242057756987858, 1.963215265680423, 1.9221127141132184, 1.9520649654961986, 1.9360988362684102, 1.9179587765214916, 1.935210302831284, 1.9148087459702234, 1.9460745953643057, 1.9526776069255911, 1.929570997336581, 1.9545171803592822, 1.9317130568317722, 1.946263369044724, 1.9855433731642784, 1.9763615548629274, 1.9251050850198614, 1.9342864468148107, 1.9082989659117469, 1.9440287187148457, 1.9361323681101459, 1.913697905639786, 1.951515699797818, 1.9235032176418405, 1.9185794671207397, 1.9363306222363907, 1.9196563208848398, 1.9280148536473256, 1.9623044755461292, 1.9167708641967172, 1.9416519549507407, 1.9173723176128081, 1.9098566103784307, 1.9270530559431076, 1.9803983815883832, 1.9437004688813848, 1.9375370996677443 ]
null
Does the given time series exhibit regime switching?
[ "No", "Yes" ]
No
binary
39
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Regime Switching" ]
Identify whether the time series exhibit different patterns over time.
Pattern Recognition
Regime Switching Detection
154
[ 0.007852518517034212, 0.06433285618864251, 0.13385725520387232, 0.01697586432483271, -0.00808563106395982, 0.0030744350327749498, 0.0911119925825232, 0.08891963036184959, 0.2385323416490452, 0.007527317610989445, 0.0562288033912495, 0.18542928795706604, 0.16311053543297444, 0.09846375918231189, 0.09237759708487395, 0.06922034472633479, 0.03620691601479188, 0.13381722615844757, 0.20384321536862085, 0.19002823032791177, 0.15201362484363296, 0.200858047018184, 0.07932995224794188, 0.24220099504746026, 0.04665589022160527, 0.2264731594966257, 0.26877341139979677, 0.22722274141953497, 0.22522417646407594, 0.12473752296765131, 0.2361370201138153, 0.45053536546608186, 0.2497305785075579, 0.23651910293066133, 0.10806068624319792, 0.3179142069680235, 0.33328712484290224, 0.24827457157272773, 0.2798430275971105, 0.17737790854730337, 0.3333644515535822, 0.3794711661401285, 0.1068875390523826, 0.3411192622654011, 0.5291325379747185, 0.376239611821307, 0.4190259971906392, 0.4847800425758584, 0.4428825451696504, 0.3564435878657546, 0.23606778582898985, 0.6843249034404524, 0.49302392068769635, 0.5110409606805033, 0.5390552735945922, 0.5550972617251074, 0.5301506302802311, 0.4600720400936588, 0.43260313566711894, 0.547754850757081, 0.49599004876545233, 0.5007440032863865, 0.5976260767444053, 0.7188413589912213, 0.4174294915761996, 0.5702093848367054, 0.42686758864004426, 0.5572504640005688, 0.3741306694591576, 0.48236794707230934, 0.5635039679969218, 0.6783937965605362, 0.426666092761954, 0.6296213969567407, 0.5898496053675925, 0.7505870764215712, 0.5683464321791936, 0.6617742972018636, 0.609669057977291, 0.5340938368159371, 0.5833317334278977, 0.5725679542457194, 0.4798346363227376, 0.8231683652619126, 0.6371892431246043, 0.7303824092455214, 0.5770405725990332, 0.7024717127610607, 0.7573603619871875, 0.7226596894671952, 0.6372572708985329, 0.8122220068816841, 0.5292106334605438, 0.5852966351797393, 0.7420756047572165, 0.5179010938035895, 0.6899861648862027, 0.7299409589007908, 0.9408866938930904, 0.7267601801001607, 0.7488234313197901, 0.6216615558874058, 0.8258132242716804, 0.8270834316749417, 0.7227956872070803, 0.9351929048458635, 0.9365767511648825, 0.8011849107104939, 0.6701132673795963, 0.7649859111392974, 1.0385022823683225, 0.9044189991845701, 0.8202244761488128, 1.0454322072216178, 0.895424524851921, 0.9236747156264957, 1.1738535825243714, 1.0321788566369194, 0.9172935554403016, 0.9920743330512736, 1.0185348850756977, 1.054112158786721, 0.8728715465845689, 1.152461011256226, 1.1395707131832322, 0.9078982290111319, 1.0090657472614206, 1.026168642124669 ]
null
Which of the following time series is more likely to be an AR(1) process?
[ "Time Series 1", "Time Series 2" ]
Time Series 1
multiple_choice
49
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "AutoRegressive Process", "Stationarity" ]
AR(1) process is a stationary process with a constant mean and variance. You should check if the time series has a constant mean and variance over time. The other option is likely not stationary.
Pattern Recognition
AR/MA recognition
155
[ 6.35242032411602, 14.755048589674967, 3.77742496940283, 2.249765540842769, 11.757624716449502, 14.699161891065332, 7.09628973840314, -9.452535875280162, -4.453446792276017, -19.349509452850704, -17.604621209192214, -2.2942129749870954, 4.132216083028817, -16.930766913698616, -11.540098487555722, -12.423777936070522, -22.676196356110914, -14.79074412686771, -16.74997378940144, -18.25531941791613, -13.363804350065703, -16.94988878906741, -12.602552692588294, -16.015291050369974, -18.1968474465214, -4.6281802642618395, 7.244172152393294, -11.291204749511614, -1.5622353611437667, 12.736121757346043, 25.7626655799126, 25.81392972278127, 10.077165200786046, 14.361540564104182, 2.79670889912418, -1.1922845422671973, 2.876993936634431, -5.745998316879847, -16.060801720681727, -8.19090819163715, 12.119515489813432, 22.127955696645383, 24.774226407817256, 29.611235482151137, 30.066183357540073, 28.551948980125985, 31.82773758008726, 11.778559143529588, 11.417700309252801, 8.324678184963807, 7.448770859416682, 1.3452945400654501, 6.281114079794682, -3.1543888330403247, -7.862060360352603, -12.828447424803088, -1.6232915671437809, -1.415868285806959, -5.741018026613196, -8.32711137145236, -6.622882930357819, 10.923442844412282, -3.6264490879368925, 3.8539407064483404, 7.801775945468751, -2.588233228092756, 12.59814631907266, 15.47132156406489, 7.299724218954346, -12.754353884299121, -7.912014762448171, -6.777067657518369, -15.728783438675888, -23.01917085229708, -9.730238010642617, -27.341139481724042, -18.730084034648872, -16.85843698082771, -8.515201301127778, -9.755211139504235, -4.871419259000243, -11.324883852487286, -9.551956243723515, -2.7507262134713857, -0.6918622580224068, 3.2495786175975176, -6.679550100438455, -11.250385699199285, -14.733706951518881, -14.551863117082632, 1.944338202512176, -1.9863169933472686, -13.024648192332817, -5.00326167536033, 9.46382497405655, -5.0410585392778495, 15.695260715758621, 16.707974595837616, 23.705287567354, 33.63494640431635, 36.63413355053051, 28.201950870347588, 34.73854213819222, 30.336831409262974, 35.17431237648678, 40.13820473899626, 21.201597891469326, 24.904289632945527, 18.104512521222706, 15.865516384818317, 13.963340596282631, 12.040374652779686, 24.867552844514144, 6.22956600667068, -12.239780763361317, -29.124725327344787, -12.608337622516695, -17.821045016689958, -12.934971318203104, -11.495148295808274, -18.059870043046423, -5.933123658880151, -6.021128609277966, -7.19425365780622, -3.4266898488424657, 4.466368969308198, -6.01666725989792, 3.9158496654145303 ]
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Weak stationarity requires the mean, variance to be constant over time. Does the following time-series exhibit weak stationarity?
[ "No, the mean is different overtime", "Yes", "No, the variance is different overtime" ]
No, the variance is different overtime
multiple_choice
33
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Stationarity" ]
For mean, check if the average value changes over time. For variance, check if the degree of variation changes over time.
Pattern Recognition
Stationarity Detection
156
[ -0.11271786690034494, -0.16543289987705023, 0.057291563133367765, 0.0439291451220162, 0.3055228664106453, -0.03954642319640324, 0.132411282813086, -0.07973868318428837, 0.05922008800223569, -0.06672214481097172, 0.10991843103873983, 0.276326220547241, -0.05763351008621237, 0.049496510096423606, 0.038022047331014844, 0.1063321639451014, -0.18223373393457415, 0.2861576043438402, -0.05836260524979004, -0.12362131185925206, -0.05492536752814993, 0.30865226082204633, -0.0180275667084192, -0.15065651387734846, 0.17772286708457058, 0.04284923743449606, 0.20920753267332579, 0.11443453489159797, -0.051664621843411876, 0.22417998001175818, -0.14865409006253377, -0.1637085736196967, -0.08708297330879594, 0.13810122507812084, -0.07466377950908354, 0.32876033064455434, -0.3998888801146372, 0.015956267567871364, -0.18753388151709197, -0.06764882281950196, 0.1191841114026454, 0.22468471601069415, -0.006860592375115077, -0.10124912042567685, -0.1533566238088824, 0.04263276970765846, -0.2948997292088936, -0.2135213722534527, -0.08456528559259716, -0.10105383475356022, 0.008062725381730848, 0.1445201854540874, 0.23343618077896724, 0.30318158272840046, 0.3125512512282902, 0.42786134370987094, -0.008329988397912172, -0.06566789938761394, 0.0661882757687009, -0.09974693251677713, 0.08488500981914779, 0.09933101757498314, 0.0916039030464846, 0.21753758006880602, -0.8121215880686827, -5.700194712183324, 6.847352976050598, -2.2632996527333846, 0.7874480317263697, -5.1215532361674505, -1.5312703207313436, 5.465366949916019, 2.081992048105598, -3.4631400373178316, -9.112036577755338, 3.581198187884162, -1.9663978892818392, 1.1232638125945997, -0.5805856315867108, -1.751925960940893, 3.08625420606002, -4.127136785397081, 0.8836251273569369, 2.1001514432660398, 6.8218129167529575, 0.376085828290336, -5.546815685222825, 3.4095219246484545, -4.151510058773747, -3.130373552904537, -2.306141677111202, -0.7264024016151104, 6.600112312266228, 3.189679530862291, -5.230793481598708, 7.527552380384896, 5.315036403422412, 2.2455579805440657, -11.111178729128083, -3.822090234526404, 0.1591750817839309, 8.027337784668715, 6.6969141153331595, -4.755663182283467, -8.044372743861283, -0.8138770057947468, 0.8973747873726335, -6.015141995050675, -4.2255315092293095, 10.463457679766123, -1.6263504592839675, -1.4383473243660234, 14.12370564359138, -1.3147921777059626, -4.632336544827086, -3.4735450590026233, 1.4718308093338979, -2.1138526381693783, -0.5738061063682791, 0.02358968951507078, 15.47910831464836, 3.6696521806968425, -4.408756823701445, -0.8388007065385144, 4.109531925261599, 5.483981851282764, -7.654911432271847, -6.861527416065354 ]
null
You are given two time series following similar pattern. One has an anomaly and the other does not. Which time series has the anomaly, and what is the likely type of anomaly?
[ "Time series 1 with speed up/down anomaly", "Time series 2 with cutoff anomaly", "Time series 1 with flip anomaly" ]
Time series 2 with cutoff anomaly
multiple_choice
74
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Linear Trend", "Speed Up/Down Anomaly", "Cutoff Anomaly", "Flip Anomaly" ]
You should first identify the time series with the anomaly. Remember, both time series share similar pattern. Then, you should check the type of anomaly based on the given definitions.
Anolmaly Detection
General Anomaly Detection
157
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The following time series has an anomaly with random large fluctuations. What is the likely pattern of the time series without the anomaly?
[ "Sawtooth wave with linear trend", "Square wave with log trend", "Sine wave with linear trend" ]
Sawtooth wave with linear trend
multiple_choice
66
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Sawtooth Wave", "Square Wave", "Linear Trend", "Log Trend", "Spike Anomaly" ]
Spikes anomaly bring constant large random fluctuations. Can you check the place where the spikes disappear and try to recover the original pattern?
Anolmaly Detection
General Anomaly Detection
158
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null
Is the given time series likely to be stationary after removing the cycle component?
[ "No", "Yes" ]
No
binary
35
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Stationarity", "Sine Wave", "Square Wave" ]
Cycle component brings the cyclic pattern to the time series. Assume this effect is removed, does the time series satisfy the stationarity condition?
Pattern Recognition
Stationarity Detection
159
[ 0.01480307548499866, 2.1795717499869487, 2.2406629868265235, 2.478543905469575, 2.5011930438954155, 2.2873285415087974, 2.537189428448181, 2.412553299424806, 2.3350159798537407, 2.3862291641262647, 2.32497979082481, 2.390658375390158, 2.2703550369379752, 2.4548568994255993, 2.5259474234167842, 2.3374884181766884, 2.4789283022834256, 2.4017564327020624, 2.5540970793547015, 2.260771646986961, 2.5683966012203245, 2.320427416968696, -2.3083966184976816, -2.3532990352250387, -2.3134821559590817, -2.4253555786573657, -2.1014073786296708, -2.3910820901435095, -2.3781957826814746, -2.0729530032767767, -2.2151695378791887, -2.3678699935342418, -2.250327748479993, -2.2560265675196147, -2.396223344049925, -2.2982891593957113, -2.220105610882845, -2.0858303888129863, -2.226359803420771, -2.2138459795609955, -2.279002765597041, -2.0829016600816246, -2.0972632027537794, -2.1035665403071495, 2.628280250088144, 2.462234367689392, 2.735754074062636, 2.637815419270684, 2.606218652626845, 2.686919071263316, 2.669487495755428, 2.5451440863150094, 2.6249794598211684, 2.4716453270267804, 2.6756526300268155, 2.805058103595607, 2.4974791734172195, 2.6948988544437875, 2.7538792611406655, 2.7736624664322336, 2.6224031522762723, 2.5917944156904333, 2.6483076524432536, 2.8310953122500253, 2.546048005020257, 2.600402230346046, -2.1134836791906912, -2.1601784922307536, -2.0617832836823706, -1.9111680558995174, -2.168007134224644, -2.057266546895023, -2.038459665886721, -2.0660978335048954, -2.1854863459576483, -2.086431040268642, -2.081136762907359, -1.9493073718900484, -1.965704477385382, -2.157856621809739, -2.036385322359764, -2.042701847077888, -1.7922114375892142, -1.9534542876322034, -2.085434087266846, -2.050374677325384, -2.0055540646861845, -1.9264674069289178, 2.8853843113346387, 2.8619358412861327, 2.8259064127519355, 2.838278265195101, 2.843976735271297, 2.6835780284826933, 2.8607352163454873, 2.8141343332289916, 2.7115720770285288, 3.1236387172092237, 2.917976180302861, 2.893151505830552, 2.8597237412417167, 2.974854146464738, 2.790597234977741, 2.880552192002735, 2.8216008932281818, 2.993969627161298, 2.844134587507471, 3.041936843554679, 2.926871883929504, 3.019243792671162, -1.8078249891637967, -1.7368910339027588, -1.9313378166065898, -1.7422624782105, -2.014088301729517, -1.8185213998966485, -1.8724309090397453, -1.9532974939103742, -1.8972002077116312, -1.866918207541657, -1.817512442825537, -1.8453468795014962, -1.7798639566640382, -1.865887025278287, -1.82556160352099, -1.8017870796263695, -1.7496754905906344, -1.6780050243413802 ]
null
Does the given time series exhibit any monotonic increasing trend?
[ "Yes", "No" ]
Yes
binary
3
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend", "Exponential Trend", "Log Trend" ]
Check if the time series values increase over time.
Pattern Recognition
Trend Recognition
160
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null
The given time series has a cyclic component and a trend component added together. What is the most likely type of the trend component?
[ "Linear", "Exponential", "Log" ]
Linear
multiple_choice
10
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend", "Exponential Trend", "Log Trend", "Sine Wave", "Additive Composition" ]
Despite having a cyclic component, check the general trend of the time series.
Pattern Recognition
Trend Recognition
161
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null
What is the most likely variance of the given time series?
[ "1", "0.7", "varies across time" ]
varies across time
multiple_choice
42
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Variance" ]
Check the degree of variation of the time series over time.
Pattern Recognition
First Two Moment Recognition
162
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null
The following time series has an anomaly with short term disruption on its pattern. What is the likely pattern of the time series without the anomaly?
[ "Square wave with log trend", "Sine wave with linear trend", "Sawtooth wave with linear trend" ]
Sine wave with linear trend
multiple_choice
73
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Sawtooth Wave", "Square Wave", "Linear Trend", "Log Trend", "Wander Anomaly" ]
Wander anomaly brings short term disruption on the pattern. You should focus on the overall pattern of the time series without the anomaly.
Anolmaly Detection
General Anomaly Detection
163
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null
Covariance stationarity in a time series means constant mean, constant variance and that autocovariance depends only on time lag, not absolute time. Is the given time series covariance-stationary?
[ "Yes", "No" ]
No
binary
37
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Stationarity", "AutoRegressive Process", "Linear Trend" ]
Check if the covariance between any two points depends only on the time distance between them.
Pattern Recognition
Stationarity Detection
164
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null
You are given two time series following similar pattern. Both of them have an anomaly. What is the likely type of anomaly in each time series?
[ "Time series 1 with flip anomaly and time series 2 with speed up/down anomaly", "Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly", "Time series 1 with cutoff anomaly and time series 2 with flip anomaly" ]
Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly
multiple_choice
75
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Sawtooth Wave", "Linear Trend", "Log Trend", "Cutoff Anomaly", "Flip Anomaly", "Speed Up/Down Anomaly" ]
You already know both time series have an anomaly. You should treat them separately and check the type of anomaly based on the given definitions.
Anolmaly Detection
General Anomaly Detection
165
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The following time series has an anomaly. What is the most likely type of anomaly?
[ "Spike: the pattern of time series is distorted by random large spikes", "Flip: the pattern is flipped at certain point in time", "Speed up/down: the period of cyclic components is different from other parts of the time series" ]
Spike: the pattern of time series is distorted by random large spikes
multiple_choice
64
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Spike Anomaly", "Flip Anomaly", "Speed Up/Down Anomaly" ]
Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions.
Anolmaly Detection
General Anomaly Detection
166
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null
The time series shows a structural break. What is the most likely cause of this break?
[ "Abrupt frequency change", "Sudden shift in trend direction", "Change in variance in underlying distribution" ]
Sudden shift in trend direction
multiple_choice
71
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend", "Gaussian White Noise", "Sine Wave" ]
You know the time series shows a structural break. Can you first identify the place where the break happens? Then, you should check the type of break based on the given options.
Anolmaly Detection
General Anomaly Detection
167
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null
Two time series are given. Both of them have a noise component. Do they have the same type of noise?
[ "Yes, they both have Gaussian white noise", "No, they have different noise" ]
No, they have different noise
binary
87
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Gaussian White Noise", "Red Noise", "Additive Composition" ]
When a white noise is added to a time series, it is expected the random fluctuations have similar amplitude or distribution. Random walk, on the other hand, can result in very different noise patterns.
Similarity Analysis
Shape
168
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Does any part of the given time series, composed of several concatenated patterns, appear to be stationary?
[ "Yes", "No" ]
No
binary
33
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Stationarity" ]
You can try to identify different parts in the time series first, and see if any part is stationary.
Pattern Recognition
Stationarity Detection
169
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null
Seasonal stationarity refers to a time series where statistical properties remain constant within seasons but may vary between seasons. Does the time series exhibit seasonal stationarity?
[ "Yes", "No" ]
No
binary
38
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Stationarity", "Sine Wave", "Linear Trend", "Gaussian White Noise" ]
Determine if the statistical properties of the series are constant within seasons across years.
Pattern Recognition
Stationarity Detection
170
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null
Which of the following best describe the cycle pattern in the given time series?
[ "Period increase over time", "Period remain the same over time", "Period decrease over time" ]
Period increase over time
multiple-choice
29
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Period" ]
Check the time interval between two peaks, and see how it changes over time.
Pattern Recognition
Cycle Recognition
171
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null
Is the given time series likely to have an anomaly?
[ "Yes, it's pattern is distorted by random spikes", "Yes, it's pattern is flipped at certain point in time", "No" ]
No
binary
63
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Flip Anomaly", "Spike Anomaly" ]
Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions.
Anolmaly Detection
General Anomaly Detection
172
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null
You are given two time series where one is the lagged version of the other. What is the most likely lagging step?
[ "Lagging step is between 60 to 75", "Lagging step is between 5 to 20", "Lagging step is between 30 to 45" ]
Lagging step is between 5 to 20
multiple_choice
98
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Lagged Pair" ]
You already know that one time series is the lagged version of the other. Shift the time series by lags proposed in the options and check which one looks the same as the other time series.
Causality Analysis
Granger Causality
173
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Are the given two time series likely to have the same underlying distribution?
[ "Yes, they have the same underlying distribution", "No, they have different underlying distribution" ]
No, they have different underlying distribution
binary
94
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "AutoRegressive Process", "Moving Average Process" ]
The difference between AR(1) and MA(1) is that AR(1) is a linear combination of past values and white noise, while MA(1) is a linear combination of past white noise values. You should check if the time series exhibit any dependency on the previous values. This could give you a clue about whether the time series is AR(1) or not. Check this for both time series.
Similarity Analysis
Distributional
174
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The given time series is a swatooth wave followed by a square wave. What is the most likely period of the swatooth wave?
[ "15.17", "35.12", "59.68" ]
59.68
multiple-choice
25
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sawtooth Wave", "Square Wave", "Period" ]
The sawtooth wave comes before the square wave. Begin by identifying where the sawtooth wave starts. Next, measure the time interval between two peaks.
Pattern Recognition
Cycle Recognition
175
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null
Is the given time series likely to have an anomaly?
[ "No", "Yes, it's pattern is distorted by random spikes", "Yes, it's pattern is flipped at certain point in time" ]
Yes, it's pattern is distorted by random spikes
binary
64
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Flip Anomaly", "Spike Anomaly" ]
Anomaly is an observation that deviates from the general pattern in the time series. You should check if the time series has any sudden changes or unexpected patterns. If so, check the type of anomaly based on the given definitions.
Anolmaly Detection
General Anomaly Detection
176
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null
Are there any granger causality between the two time series?
[ "No, they are not granger causality", "Yes, time series 1 granger causes time series 2", "Yes, time series 2 granger causes time series 1" ]
Yes, time series 1 granger causes time series 2
binary
105
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Granger Causality" ]
Granger causality is a statistical concept that determines whether one time series can predict another. While you cannot perform the statistical test, you can check if one time series can predict the other by shifting the time series by a certain number of steps. Do they look simiar after the shift?
Causality Analysis
Granger Causality
177
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Are there any granger causality between the two time series?
[ "Yes, time series 1 granger causes time series 2", "Yes, time series 2 granger causes time series 1", "No, they are not granger causality" ]
Yes, time series 2 granger causes time series 1
binary
105
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Granger Causality" ]
Granger causality is a statistical concept that determines whether one time series can predict another. While you cannot perform the statistical test, you can check if one time series can predict the other by shifting the time series by a certain number of steps. Do they look simiar after the shift?
Causality Analysis
Granger Causality
178
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You are given two Autoregressive processes AR(1). Which of the following time series has higher standard deviation for their random component?
[ "Time series 2", "Time series 1" ]
Time series 1
multiple_choice
61
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "AutoRegressive Process", "Variance" ]
The standard deviation of the noise component is related to the average distance between the data points and their past values. You should check the degree of variation of the time series over time. Which time series has a higher change in average?
Noise Understanding
Signal to Noise Ratio Understanding
179
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In which part of the time series does the anomaly occur?
[ "Beginning", "Middle", "End" ]
Beginning
multiple_choice
78
medium
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Linear Trend", "Spike Anomaly", "Cutoff Anomaly", "Wander Anomaly" ]
Identify where in the time series sequence the unusual pattern or disruption occurs.
Anolmaly Detection
General Anomaly Detection
180
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null
Is time series 1 a lagged version of time series 2?
[ "No, they do not share similar pattern", "No, time series 2 is a lagged version of time series 1", "Yes" ]
Yes
multiple_choice
97
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Lagged Pair" ]
Focus on the time delay between the two time series. If time series 1 is a lagged version, then it should look the same to time series 2 after being shifted by a certain number of steps. Can you check this?
Causality Analysis
Granger Causality
181
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You are given two time series following similar pattern. Both of them have an anomaly. What is the likely type of anomaly in each time series?
[ "Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly", "Time series 1 with flip anomaly and time series 2 with speed up/down anomaly", "Time series 1 with cutoff anomaly and time series 2 with flip anomaly" ]
Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly
multiple_choice
74
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Sawtooth Wave", "Linear Trend", "Log Trend", "Cutoff Anomaly", "Flip Anomaly", "Speed Up/Down Anomaly" ]
You already know both time series have an anomaly. You should treat them separately and check the type of anomaly based on the given definitions.
Anolmaly Detection
General Anomaly Detection
182
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You are given two time series following similar pattern. Both of them have an anomaly. What is the likely type of anomaly in each time series?
[ "Time series 1 with cutoff anomaly and time series 2 with flip anomaly", "Time series 1 with flip anomaly and time series 2 with speed up/down anomaly", "Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly" ]
Time series 1 with cutoff anomaly and time series 2 with speed up/down anomaly
multiple_choice
75
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Sawtooth Wave", "Linear Trend", "Log Trend", "Cutoff Anomaly", "Flip Anomaly", "Speed Up/Down Anomaly" ]
You already know both time series have an anomaly. You should treat them separately and check the type of anomaly based on the given definitions.
Anolmaly Detection
General Anomaly Detection
183
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You are given two time series following similar pattern. Both of them have an anomaly. Do they have the same type of anomaly?
[ "No. They have different types of anomalies", "Yes, Time series 1 and time series 2 both have flip anomaly" ]
No. They have different types of anomalies
binary
77
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Cutoff Anomaly", "Flip Anomaly", "Spike Anomaly" ]
For each time series, identify the type of anomaly based on the given definitions. Then, check if they have the same type of anomaly.
Anolmaly Detection
General Anomaly Detection
184
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The following time series has two types of anomalies appearing at different time points. What are the likely types of anomalies?
[ "speedup and cutoff", "cutoff and flip", "speedup and flip" ]
speedup and flip
multiple_choice
68
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Cutoff Anomaly", "Flip Anomaly", "Speed Up/Down Anomaly" ]
You should first identify the two places where the anomalies appear. Then, you should check the type of anomaly based on the given definitions.
Anolmaly Detection
General Anomaly Detection
185
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7.968303039581183, 8.414084441983647 ]
null
Is the given time series likely to be stationary after removing the trend?
[ "No", "Yes" ]
Yes
binary
34
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Stationarity", "Linear Trend", "Exponential Trend" ]
Trend brings the overall shape of the time series up or down. Assume this effect is removed, does the time series satisfy the stationarity condition?
Pattern Recognition
Stationarity Detection
186
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null
You are given two time series which both have trend components. Do they have the same type of trend?
[ "No, time series 1 has linear trend and time series 2 has exponential trend", "No, time series 1 has exponential trend and time series 2 has log trend", "Yes, they both have exponential trend" ]
No, time series 1 has linear trend and time series 2 has exponential trend
multiple_choice
85
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend", "Exponential Trend", "Log Trend" ]
First identify the trend component for each time series. Then, check if they are equal.
Similarity Analysis
Shape
187
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Does the given two time series have similar pattern?
[ "No, they have different shape", "Yes, they have similar shape" ]
No, they have different shape
binary
79
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Square Wave" ]
Pattern refers to the general shape of the time series. In this case, you see both time series have cyclic patterns. Do their behaviors at peak and trough look similar?
Similarity Analysis
Shape
188
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What is the most likely autocorrelation at lag 1 for the given time series?
[ "Negative autocorrelation", "High positive autocorrelation", "No autocorrelation" ]
Negative autocorrelation
multiple_choice
45
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Autocorrelation" ]
While it is hard to directly measure the autocorrelation for higher order lags, the autocorrelation at lag 1 can be approximated by observing the time series pattern. You can tell this by checking the sign and magnitude changes at each step compared to the previous step.
Pattern Recognition
AR/MA recognition
189
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null
Is the given time series stationary?
[ "No", "Yes" ]
Yes
binary
30
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Stationarity" ]
Try to see if the time series has a constant mean, and degree of variation over time.
Pattern Recognition
Stationarity Detection
190
[ -0.8031559008424695, -0.8286176044121526, 0.7169758762882232, 1.0587418493542933, -0.05033502043435413, -3.05641033099585, -0.5026629295751975, 0.23369302950964918, -0.5392479785020693, -0.025713336250698396, 1.2046581593978731, 0.6308303981938984, 0.41445943143797326, 0.029083211432675955, -0.22966006595906396, 0.384815139149656, -0.01573685797948307, 0.041878853037966686, -0.9137388835967686, -0.3176430481788596, 0.510444412219774, -0.7511670016226129, 0.030611510314687063, 0.2824603296910756, 0.5175944667036596, 0.4979801189960432, 0.430873549493145, -0.3804598954387452, -1.0353725740443482, -0.6109785380082613, 0.4954480421980075, -0.7757516561437992, -0.584217060584323, -0.5927277178452404, -1.1636060572791944, -0.855272018108062, 0.5626678273028713, -0.5608130561610243, -1.128207648904143, -0.5395741290051645, 0.33559232687879975, -1.0647874216123405, 0.6944831755628222, -0.9840277561182297, -0.33027049016704496, -0.1882906321387302, -0.33912970763499867, -0.06731827243740679, 0.4538162550448873, -0.019231499691912846, -0.4291688375261458, 0.37962400627519327, 0.2606100179495896, -0.5776251950611155, -0.09472444148605555, -0.3606480116913606, -0.5535485904291841, 0.417493692505269, 0.3926689767271528, 0.5415004571807328, 0.6385871631925959, -0.6078066123443993, 0.7702625851101386, -0.34087200312682053, 0.7515372689638566, 0.012020715941353205, 0.36666766010704377, 0.33709008862092416, 0.525626646748757, -1.1935396910976763, -1.0983388083141596, 0.2662387899059552, 0.14259187584072203, -0.4682124173302101, 0.23513960568038098, -0.9495683563802857, 2.0703809489305796, 0.6864927770972089, 0.42802447809596395, 0.08772224655045759, -0.5066184557224204, -0.9161645191328471, 0.0792556847992217, -0.45024382850728895, -0.6762303610458474, -0.9414658520189932, 0.3099106650085402, -0.2699104088081113, 0.32547598214740037, 0.4102952466764426, 0.24982039143165252, 1.844899325678046, 0.516251121354112, -0.7638543894636073, 0.4515745092953832, -0.04056130353040417, -0.9238032002080577, -0.813206187553549, 0.5029998428020584, 0.6708690034915317, -0.7264617077836072, 0.6114975213606398, 0.08471356517511947, 0.7993358561872169, 0.6101554114724032, -0.15700165365084018, 1.1472338666428603, -0.7012866315024657, 1.3930514704871828, -0.4216932987195785, -0.1403117103578757, -0.4696140967238733, -0.37202715647285267, 1.8591720044211635, -0.3944044086060982, 0.4732523492207572, -0.4014513152288284, 0.6297561261817531, -0.33514863100654235, 0.39311594487752394, 0.46266209114395723, -0.5250063714232021, 1.6960233195204861, 0.43880469774444586, -0.10394042510516455, -0.5094972396790998, 0.0665056139178298, -0.6110576156025231 ]
null
You are given two time series which both have trend components. Do they have the same type of trend?
[ "No, time series 1 has exponential trend and time series 2 has log trend", "No, time series 1 has linear trend and time series 2 has exponential trend", "Yes, they both have exponential trend" ]
No, time series 1 has exponential trend and time series 2 has log trend
multiple_choice
86
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend", "Exponential Trend", "Log Trend" ]
First identify the trend component for each time series. Then, check if they are equal.
Similarity Analysis
Shape
191
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What type of trend does the time series exhibit in the latter half?
[ "No trend", "Exponential", "Linear" ]
Linear
multiple_choice
14
medium
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend", "Exponential Trend" ]
Focus on the pattern of growth or decline in the second half of the time series.
Pattern Recognition
Trend Recognition
192
[ 0.8618022625327343, 1.1171821178337318, 1.1156173839576717, 1.0556960487167035, 1.0986946148378969, 1.0280756909104038, 1.073214376967081, 0.9011993783908864, 1.174804062690963, 0.9915793078344262, 1.0320130681080608, 1.2982119277016273, 1.0321100454569025, 1.1326902397807541, 1.1148629907975949, 0.9348080748504435, 0.9728164245342594, 1.281101074611363, 1.140170764876707, 1.0853301016735701, 1.0382368897267404, 1.1286146033049664, 1.1429479185294529, 1.128932838526375, 1.2180456164676416, 1.0293540025167025, 1.020881542226632, 1.230192177803905, 1.3421090226960888, 1.2200764918580287, 1.2705914865885866, 1.1383529202342515, 1.177491452213856, 1.1812107448754448, 1.155489898486719, 1.3072720933048745, 1.282859466682683, 1.1365062377497095, 1.0170495616993804, 1.1804502350582633, 1.0765657270369862, 1.19964472220225, 1.0949599845297633, 1.3334907922757382, 1.281597245429874, 1.2845073717907054, 1.331075536645701, 1.2683540184410669, 1.289333519897871, 1.3835570716725694, 1.2320934579637355, 1.3115382968798384, 1.151218831142378, 1.2699905245904495, 1.493545694845428, 1.3546787965433733, 1.2702003163112756, 1.3108819603886384, 1.2879809942875582, 1.3189286821880082, 1.261560724155984, 1.3566764168253482, 1.3659733871761814, 1.3193846114039156, 1.3327267409774297, 1.3550351215774492, 1.2704715948700442, 1.3739604747173264, 1.4056867566470725, 1.4763951392107884, 1.36398331720507, 1.2788200270205057, 1.3263274336290976, 1.224562297724052, 1.413831504981694, 1.1373612677058555, 1.4480368530610523, 1.4623413042413984, 1.0569838940626601, 1.4798120642711228, 1.4572501255621784, 1.3277538738986885, 1.2398150267673798, 1.2774582295186923, 1.2876199035055313, 1.4973148228036743, 1.3230427511483636, 1.4607166236680407, 1.3600157899057834, 1.3801669651697839, 1.2197573855289157, 1.264427474046988, 1.510681784379123, 1.3680373237471797, 1.415913064421411, 1.56872037910897, 1.3775595889455663, 1.3741351525641103, 1.5154058828283912, 1.3982698067258874, 1.4312730840590133, 1.5011915539589984, 1.4037589330210527, 1.4847438596190288, 1.396637928686992, 1.5085740149178861, 1.454830350151178, 1.39239478746463, 1.4245976887025051, 1.1749770260620924, 1.5410251835815776, 1.2984219128957326, 1.3192111389631376, 1.3845585924926114, 1.251302934582401, 1.4795971639441452, 1.4711744800567468, 1.3886567197718898, 1.3717441805513642, 1.372446056811888, 1.5055030951802273, 1.2868882121876122, 1.4075984234993633, 1.297793113057654, 1.5298478741609858, 1.4215887309177708, 1.5121234513740365, 1.5846398235517931 ]
null
You are given two time series which both have upward trend. Which time series has a higher slope in terms of magnitude?
[ "Time series 1 has higher slope", "Time series 2 has higher slope" ]
Time series 2 has higher slope
binary
80
easy
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend", "Exponential Trend", "Sine Wave", "Sawtooth Wave" ]
Slope refers to the steepness of the trend. You should check the direction of the trend and the steepness of the trend. If the trend is upward, you should check the magnitude of the slope.
Similarity Analysis
Shape
193
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The time series shows a structural break. What is the most likely cause of this break?
[ "Sudden shift in trend direction", "Abrupt frequency change", "Change in variance in underlying distribution" ]
Change in variance in underlying distribution
multiple_choice
71
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend", "Gaussian White Noise", "Sine Wave" ]
You know the time series shows a structural break. Can you first identify the place where the break happens? Then, you should check the type of break based on the given options.
Anolmaly Detection
General Anomaly Detection
194
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null
Does time series 1 granger cause time series 2?
[ "No, they are not granger causality", "Yes, time series 1 granger causes time series 2", "No, time series 2 granger causes time series 1" ]
Yes, time series 1 granger causes time series 2
binary
103
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Granger Causality" ]
Granger causality is a statistical concept that determines whether one time series can predict another. While you cannot perform the statistical test, you can check if one time series can predict the other by shifting the time series by a certain number of steps. Do they look simiar after the shift?
Causality Analysis
Granger Causality
195
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Two time series are given. Both of them have a noise component. Do they have the same level of noise?
[ "Yes, they both have the same level of noise", "No, they have different level of noise" ]
Yes, they both have the same level of noise
binary
88
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Gaussian White Noise", "Variance" ]
Noise level refers to the amplitude of the random fluctuations in the time series. Both time series have a white noise component added to it. You should check the amplitude of the noise for both time series.
Similarity Analysis
Shape
196
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The given time series has square wave pattern. How does its period change from the beginning to the end?
[ "Remain the same", "Increase", "Decrease" ]
Remain the same
multiple-choice
19
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Square Wave", "Period" ]
Base on the definition of period, check if the time interval between two peaks remains the same.
Pattern Recognition
Cycle Recognition
197
[ 0.062411981705215516, 2.407871463080505, 2.3438122348693855, 2.255311475005494, 2.35261736797345, 2.2773207410028657, 2.442548885495852, 2.330331174003863, 2.262487192474826, 2.3128983279887776, 2.3863300575816395, 2.2886644568736796, 2.262814872597434, 2.3694056333032685, 2.3695335692649495, 2.294342594616964, 2.297933081592245, 2.3682419058898407, 2.2002284780043446, 2.2042905347164217, 2.2731924900288334, 2.3236921969828925, -2.3139461555942766, -2.197501290459122, -2.259270949833875, -2.36103076515042, -2.3469385329443457, -2.445289848617858, -2.346888225753316, -2.3739027760460907, -2.312765056120268, -2.4277600065093092, -2.29310226072996, -2.1917630208538195, -2.3559129269997627, -2.3048657399441828, -2.2760225129829657, -2.3851589593426605, -2.3226276639730354, -2.3437776720758974, -2.335269302299194, -2.4223378905396236, -2.342585894728183, 2.3948367412786222, 2.490151272933581, 2.4409639947625976, 2.560355157905233, 2.268302155865272, 2.432268975826145, 2.3633711127279122, 2.564017205475844, 2.2642070836185617, 2.2610647279359997, 2.2850976476096747, 2.1326473397230963, 2.292461409986001, 2.2691236459987074, 2.3600762908016977, 2.379212509731793, 2.5326539960756658, 2.4400792959726822, 2.2873465465878366, 2.255195445019241, 2.3942288293047276, -2.4770602328561413, -2.1618910355686416, -2.2270929000819484, -2.3919544773645476, -2.5163503650631647, -2.209649674737536, -2.3564908966793388, -2.221255280956731, -2.5044796780335137, -2.4049744144494545, -2.3445125421822586, -2.340338852777603, -2.3900434593020012, -2.282751918919327, -2.4517989550923365, -2.3592748606562064, -2.3330073489828873, -2.293593028748202, -2.273875424345188, -2.457501121337864, -2.498448329227639, -2.2172692299642263, 2.378268313352036, 2.270188258498422, 2.5001521097063293, 2.356604375583363, 2.4629666305604596, 2.3517887602950878, 2.5511117046422758, 2.5205709963983973, 2.32014049730617, 2.4421940072495127, 2.409574507112592, 2.4819000679073118, 2.248544566096067, 2.413642058153921, 2.450879360839036, 2.1691629635117655, 2.2267110608874994, 2.141113694378067, 2.318096228709621, 2.4167911377336733, 2.49527261736368, -2.3376274341120995, -2.182175357596948, -2.483047057975566, -2.5153751560895925, -2.350591682043739, -2.3066303672601465, -2.348306386963486, -2.5517811221580646, -2.353948916105356, -2.4754838622045625, -2.278069657271073, -2.3083770875443923, -2.4390248907868126, -2.3964236038877464, -2.4509582643429724, -2.3513048218813943, -2.2495226801039534, -2.4436095167876313, -2.2946322606022926, -2.3980626739913213, -2.4243241953803114 ]
null
What is the most dominant pattern in this complex time series?
[ "Seasonality", "Noise", "Trend" ]
Noise
multiple_choice
13
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Linear Trend", "Sine Wave", "Gaussian White Noise" ]
Identify which component (trend, seasonality, or noise) has the largest impact on the overall pattern.
Pattern Recognition
Trend Recognition
198
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null
The following time series has an anomaly with short term disruption on its pattern. What is the likely pattern of the time series without the anomaly?
[ "Sawtooth wave with linear trend", "Square wave with log trend", "Sine wave with linear trend" ]
Square wave with log trend
multiple_choice
72
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Sawtooth Wave", "Square Wave", "Linear Trend", "Log Trend", "Wander Anomaly" ]
Wander anomaly brings short term disruption on the pattern. You should focus on the overall pattern of the time series without the anomaly.
Anolmaly Detection
General Anomaly Detection
199
[ 0.04967141530112327, 0.07179331851347275, 0.23277487032420066, 0.3996971561172212, 0.3005794410715708, 0.374583280181079, 0.6274925598694775, 0.6156154218272265, 0.5590915613751694, 0.7254559004220134, 0.6881292407134054, 0.7493858981537594, 0.8799574837232762, 0.7226400781029316, 0.7981705588186427, 0.9696918504686329, 0.9785307234268336, 1.1638325887983814, 1.09296142494668, 1.0927083443606767, 1.4295504854523406, -1.3535317485208873, -1.3711378257876192, -1.5663134805658576, -1.5232772179574003, -1.5018375453523942, -1.6712466268831865, -1.560955469124464, -1.7001596700807926, -1.7100584312158345, -1.781104231493585, -1.5750285156643629, -1.8002326275009608, -1.942608599263176, -1.79188862763238, -2.032905929071095, -1.926007221758829, -2.178345909986917, -2.1501148979754827, -2.0319775366042565, -2.0116514009248587, 2.1359525958040697, 2.1400675020538418, 2.1538522299715845, 2.067968578473751, 2.175236032293395, 2.2321107597925782, 2.4144080158488834, 2.3731576996478285, 2.1921823144647554, 2.4301865671937124, 2.3881737253460513, 2.3875156270118927, 2.5445331748364, 2.614264444218808, 2.6317417047412786, 2.481800087432396, 2.561576066384735, 2.652074495364497, 2.742636674091019, 2.6229894083485714, -2.7149952996136273, -2.832290354618452, -2.866216607165813, -2.6900006003875583, -2.6600108675014997, -2.826948110777316, -2.74324246855297, -2.831022946860857, -2.9708264377548335, -2.9090558046399297, -2.83003077531988, -3.0258192795670014, -2.9039425894369364, -3.3603244912120287, -3.053880071264075, -3.164867703178546, -3.2407612820137732, -3.2387628302919484, -3.4835684387014685, -3.3434495915314466, 2.9831402417222677, 3.099918099502804, 2.904810758200406, 2.8801103936560413, 2.9149223305001515, 3.0605961598017126, 3.0057118402110063, 2.9392569812868015, 3.0627851011351668, 3.0402235385320657, 3.146272686341564, 2.9979330929104964, 3.053942975015876, 3.0659124252456786, 2.977031683703365, 3.1711035894973647, 3.185556412666642, 3.1777749291085704, 3.1714733172622824, 3.0709214702993917, -3.8719099196160185, -3.8813662192830414, -3.944436568852386, -3.8973182876045693, -3.85763436287237, -3.726141458254228, -3.913895698852974, -3.9220666245190716, -3.971611066926312, -4.172266871687224, -3.999144669678496, -4.006455804967626, -3.7820238867878144, -4.063338874047338, -4.029589773422371, -4.078746073273648, -4.20756332118453, -3.991725630334883, -4.046020284683124, -4.057210828448606, 3.460371918682382, 3.7064843592142642, 3.4408130970655977, 3.6543775594799786, 3.8293327639332633, 3.525731930981227, 3.5825584440862874 ]
null
Which of the following best describe the cycle pattern in the given time series?
[ "Amplitude increase over time", "Amplitude decrease over time", "Amplitude remain the same over time" ]
Amplitude decrease over time
multiple-choice
28
hard
Please answer the question and provide the correct option letter, e.g., A), B), C), D), and option content at the end of your answer. All information need to answer the question is given. If you are unsure, please provide your best guess.
[ "Sine Wave", "Amplitude" ]
Check the distance between the peak and the baseline, and see how it changes over time.
Pattern Recognition
Cycle Recognition
200
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