Two approaches
There are two ways on how to deal with temporal structured input for classification, regression, clustering, forecasting and related tasks:
- Dedicated Time Series Model: The machine learning algorithm incorporates such time series directly. Such a model is like a black box and it can be hard to explain the behavior of the model. Example are autoregressive models.
- Feature based approach: Here the time series are mapped to another, possibly lower dimensional, representation. This means that the feature extraction algorithm calculates characteristics such as the average or maximal value of the time series. The features are then passed as a feature matrix to a "normal" machine learning such as a neural network, random forest or support vector machine. This approach has the advantage of a better explainability of the results. Further it enables us to use a well developed theory of supervised machine learning.
tsfresh calculates a huge number of features
The python package tsfresh calculate a huge number of such features from a pandas.DataFrame containing the time series. You can find its documentation at http://tsfresh.readthedocs.io.
Disclaimer: I am one of the authors of tsfresh.
