metadata
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.
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
| 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
View Model Plot
