timeseries-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.

Full credits to: Pavithra Vijay

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) 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

Model Plot

<details> <summary>View Model Plot</summary>

</details>