The powerful feature extraction method you’ve never heard of

MACHINE LEARNING IN PYTHON

Time series feature engineering is a very complex area because you are trying to take something that is of a long, possibly variable length, and transform it into a short fixed-length vector to be compared with other time series. This challenge has been tackled with various creative ideas and methods. One of these methods that is rarely mentioned, yet very powerful, is extracting autoregressive (AR) model coefficients.

The AR model is a classical time series model. It is relatively simple compared to modern larger neural networks like recurrent neural networks, convolutional neural networks or transformers, but is still today very competitive, especially for smaller datasets. It works by simply forecasting future values of a time series by using a linear sum of past lags. We have:

Okay, so it is a time series model, what has this to do with feature extraction? Here comes the ingenuity.. when you fit a model to a time series it learns a model of fixed…