readme: ---
  library_name: 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>
  
  ![Model Image](./model.png)
  
  </details>