Ts features

Correlation structure, distribution, entropy, stationarity and scaling properties are some of the examples for time series features and they facilitate to fit time series into a range of time series models.

Lag features are the classical way that time series forecasting problems are transformed into supervised learning problems . The simplest approach is to predict the value at the next time (t+1) given the value at the previous time (t-1).

Time-series data have core components like seasonality, trend, and cycles . For example, ice-cream sales usually have yearly seasonality — you can reasonably predict the next summer’s sales based on this year’s. Similarly, temperatures or air quality measurements have daily seasonality or also, yearly.

Rolling is a very useful operation for time series data. Rolling means creating a rolling window with a specified size and perform calculations on the data in this window which, of course, rolls through the data .