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---
library_name: tf-keras
tags:
- tabular-regression
- time-series
- anomaly-detection
---
## Timeseries anomaly detection using an Autoencoder
This repo contains the model and the notebook to this [Keras example on Timeseries anomaly detection using an Autoencoder.](https://keras.io/examples/timeseries/timeseries_anomaly_detection/)
Full credits to: [Pavithra Vijay](https://github.com/pavithrasv)
## Background and Datasets
This script demonstrates how you can use a reconstruction convolutional autoencoder model to detect anomalies in timeseries data. We will use the [Numenta Anomaly Benchmark(NAB)](https://www.kaggle.com/datasets/boltzmannbrain/nab) dataset. It provides artifical timeseries data containing labeled anomalous periods of behavior. Data are ordered, timestamped, single-valued metrics.
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
## Training Metrics
| Epochs | Train Loss | Validation Loss |
|--- |--- |--- |
| 1| 0.011| 0.014|
| 2| 0.011| 0.015|
| 3| 0.01| 0.012|
| 4| 0.01| 0.013|
| 5| 0.01| 0.012|
| 6| 0.009| 0.014|
| 7| 0.009| 0.013|
| 8| 0.009| 0.012|
| 9| 0.009| 0.012|
| 10| 0.009| 0.011|
| 11| 0.008| 0.01|
| 12| 0.008| 0.011|
| 13| 0.008| 0.009|
| 14| 0.008| 0.011|
| 15| 0.008| 0.009|
| 16| 0.008| 0.009|
| 17| 0.008| 0.009|
| 18| 0.007| 0.01|
| 19| 0.007| 0.009|
| 20| 0.007| 0.008|
| 21| 0.007| 0.009|
| 22| 0.007| 0.008|
| 23| 0.007| 0.008|
| 24| 0.007| 0.007|
| 25| 0.007| 0.008|
| 26| 0.006| 0.009|
| 27| 0.006| 0.008|
| 28| 0.006| 0.009|
| 29| 0.006| 0.008|
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> |