[prostodata]

Anomaly detection belongs to unsupervised learning while in time series analysis we normally think about future and future values are viewed as labels. One approach to think in terms of anomaly detection is to train a normal forecasting model. An anomaly is then viewed as large deviation from predicted values. Another approach to train an autoencoder on segments of the time series. Then anomaly is defined by the degree of deviation of the decoded segment from the real segment.