Feature Selection for Time Series Data – Methods and Techniques

cross correlationfeature selectiontime series

I am looking for methods for feature selection (or feature extraction) for time series data. Of course I did some research before, but it was not satisfying.

I am aware of methods like PCA, importance matrix from random forest, linear regression, etc. for feature selection or extraction, but are those methods also applicable to time series data?

The task would be to find a set of variables which is a good predictor of a certain time series variable.

Thanks for any suggestions!

Best Answer

The Cross Correlation function will help you identify relationships in your X variables. Box-Jenkins discussed this in their text book. Time Series Analysis: Forecasting and Control

Of course, you will also need to identify outliers as the relationship can be impacted by these events along with changes in trend and level.

Plotting the Y and X in standardized form in a scatterplot and line plot will also support your hypothesis.

Related Question