Ts features

Feature extraction for machine learning and deep learning. Feature extraction refers to the process of transforming raw data into numerical features that can be processed while preserving the information in the original data set . It yields better results than applying machine learning directly to the raw data.

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 .