metadata

dataset_info:
  features:
    - name: start
      dtype: timestamp[s]
    - name: feat_static_cat
      sequence: uint64
    - name: feat_dynamic_real
      sequence:
        sequence: float32
    - name: item_id
      dtype: string
    - name: target
      sequence: float64
  splits:
    - name: train
      num_bytes: 18154839
      num_examples: 210
    - name: validation
      num_bytes: 18235479
      num_examples: 210
    - name: test
      num_bytes: 18316119
      num_examples: 210
  download_size: 47737715
  dataset_size: 54706437
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Dataset Card for "kdd210_hourly"

More Information needed

Download the Dataset:

from datasets import load_dataset

dataset = load_dataset("LeoTungAnh/kdd210_hourly")

Dataset Card for Air Quality in KDD cup 2018

Originally, the dataset is from KDD cup 2018, which consists of 270 time series data with different starting time. This dataset encompasses 210 hourly time series data points starting from 2017-01-01T14:00:00. The dataset reveals the air quality levels in 59 stations in 2 cities from 01/01/2017 to 31/03/2018.

Preprocessing information:

Grouped by hour (frequency: "1H").
Applied Standardization as preprocessing technique ("Std").
Preprocessing steps:
1. Standardizing data.
2. Replacing NaN values with zeros.

Dataset information:

Missing values are converted to zeros.
Number of time series: 210
Number of training samples: 10802
Number of validation samples: 10850 (number_of_training_samples + 48)
Number of testing samples: 10898 (number_of_validation_samples + 48)

Dataset format:

  Dataset({
  
      features: ['start', 'target', 'feat_static_cat', 'feat_dynamic_real', 'item_id'],
      
      num_rows: 210
      
  })

Data format for a sample:

'start': datetime.datetime
'target': list of a time series data
'feat_static_cat': time series index
'feat_dynamic_real': None
'item_id': name of time series

Data example:

{'start': datetime.datetime(2017, 1, 1, 14, 0, 0),
 'feat_static_cat': [0],
 'feat_dynamic_real': None,
 'item_id': 'T1',
 'target': [ 1.46812152,  1.31685537,  1.26169969, ...,  0.47487208, 0.80586637,  0.33006964]
}

Usage:

The dataset can be used by available Transformer, Autoformer, Informer of Huggingface.
Other algorithms can extract data directly by making use of 'target' feature.