Autoencoder Forest for Anomaly Detection from IoT Time Series
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The document discusses the use of autoencoder forests for anomaly detection in IoT time series data, highlighting the inefficiencies of manual monitoring and traditional rule-based methods. It explains the structure and training process of autoencoding neural networks, particularly in handling time series analysis and anomaly scoring. Experiment results are presented, demonstrating its application in various monitoring scenarios, such as cooling tower temperatures and energy consumption.
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Implications for Previous and Future Research](https://image.slidesharecdn.com/2-190718140733/85/Autoencoder-Forest-for-Anomaly-Detection-from-IoT-Time-Series-12-320.jpg)
Autoencoder Forest for Anomaly Detection from IoT Time Series
- 1. <Title> Autoencoder Forest for Anomaly Detection from IoT Time Series Yiqun Hu, SP Group Data Council Singapore 2019
- 2. Agenda • Condition monitoring & anomaly detection • Autoencoder for anomaly detection • Autoencoder Forest • End-to-end workflow • Experiment results
- 3. Conditional monitoring & Anomaly Detection
- 5. • Manual monitoring – Huge human effort – Boring task with low quality • Rule-based method – Cannot differentiate different environment – Cannot adapt to different condition of the equipment • Data-driven method – Model the common behavior of the equipment Time-series anomaly detection
- 6. Autoencoder for Anomaly Detection
- 7. Autoencoder • What is autoencoder – A encoder-decoder type of neural network architecture that is used for self-learning from unlabeled data • The idea of autoencoder – Learn how to compress data into a concise representation to allow for the reconstruction with minimum error • Different variants of autoencoder – Variational Autoencoder – LSTM Autoencoder – Etc. Autoencoder Neural Network
- 8. Autoencoder for anomaly detection Online Detection Anomaly score Offline Training Reconstruction errors
- 10. A key challenge of autoencoder Single Autoencoder
- 11. The idea of autoencoder forest x x x xx x x x x o o o o o o o o o + ++ + + + +
- 12. Clustering subsequence is meaningless [1]. Eamonn Keogh, Jessica Lin, Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
- 13. Autoencoder forest based on time 0:00 1:00 1:30 22:00 23:30
- 14. Training autoencoder forest Input Layer Encoder layer 1 (window_size, 1) (window_size/2, 1) Encoder layer 2 (window_size/4, 1) Decoder layer 1 (window_size/2, 1) Decoder Layer 2 (window_size, 1) • Structure is fixed for every autoencoder. (try to make it as generic as possible) • Each autoencoder within forest is independent. So the training is naturally parallelizable • Using early stopping mechanism, the training of individual autoencoder can be stopped at similar accuracy.
- 15. Autoencoder Forest Single Autoencoder Autoencoder Forest
- 17. Automatic end-to-end workflow Time series analysis Train Data Preprocessing Train Window Extraction Autoencoder Forest Training Test Data Preprocessing Test Window Extraction Anomaly scoring Training Anomaly detection
- 18. Periodic pattern analysis • Automatic determine the repeating period in time series – Calculate autocorrelations of different lags – Find the strong local maximum of autocorrelation – Calculate the interval of any two local maximum – Find the mode of intervala
- 19. Missing data handling 3:05 3:10 3:15 3:20 … … 16:15 16:21 16:24 16:30 … … Misalignment Missing 3:05 3:10 3:15 3:20 … … 16:15 (16:20 – 16:40) 16:45 … … ? ? ? • No need to impute • If missing gap is small, impute with neighbouring points; • If missing gap is large, impute with the same time of other periods;
- 20. Anomaly scoring Extract the sequence window end at time t ...... Median profile Corresponding autoencoder reconstruct the sequence window at time t Compute reconstruction error as anomaly score Learned autoencoder forest
- 22. Cooling tower – return water temperature
- 23. Chiller – chilled water return temperature
- 24. Smart meter – half hour consumption 2018-12-03 22:00:00 Normal data 2018-09-27 14:30:00 2018-10-06 22:30:00 2018-09-07 15:30:00 Top 3 Detected Anomaly
- 26. A common platform for time series data, with built-in AI capabilities