For high frequency, high resolution temporal data, it maybe beneficial to create aggregate feature values for different intervals and create a new data frame with these aggregated statistical values replacing the original column. This new data frame can then be used for different machine learning tasks.

This could be very useful in IOT data, where sensor observations are often high frequency and have noise in them.

A classical way is to apply ``mean'' primitive for each user specified interval of time for a numeric column and create a time series. Since mean itself is a statistical feature, this can be considered as feature time series.

If this can be made possible via a simple ft.dfs call, it could prove very useful.