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.

Hi, yes I'd love to add in anomaly detection, motifs and discords, will take time though, lack of people!

Is this not a timeseries classification, which they do implement?

> Is anomaly detection not a timeseries classification, which they do implement

It can be a time series classification if and only if you have labels that say a given sample is an anomaly. But then what if it runs for a while and a new kind of anomaly starts happening that it’s never seen before and isn’t in the training set: will it detect it based on your labels?

Ideally anomaly detection should be a more unsupervised learning scenario where it can automatically determine what’s normal and what’s not.

I knew about work in anomaly detection for state of health and state of charge in batteries, where you can somewhat model the physical effects (in a data driven manner). However, this description of the problem made me think that meta-learning might be suitable for the problem you’re describing. I’ve only seen it applied in computer vision though (and more recently in speech).

Anomaly detection might also refer to outlier detection, in that "an anomaly occurred at this point in the time series."