I'd like to develop a set of models for anomaly detection of multiple time series. After some reading, I have found a few promising approaches, such as
- Segmentation-based approaches (SECODA);
- Artificial neural network based (auto encoders);
- Recurrent neural network approaches such as Long Short Term Memory.
By the way, if you see some good technique that I have missed, let me know!
All these approached are unsupervised, and I understand that because in typical applications the anomalies may be rare events. Nonetheless, I wouldn't like to lose the information contained in those small known cases. So I was wondering, are there any supervised approaches for anomaly detection of multiple time series?