What does rolling mean in time?

A rolling window model involves calculating a statistic on a fixed contiguous block of prior observations and using it as a forecast . It is much like the expanding window, but the window size remains fixed and counts backwards from the most recent observation.

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.

Feature engineering efforts mainly have two goals: Creating the correct input dataset to feed the ML algorithm: In this case, the purpose of feature engineering in time series forecasting is to create input features from historical row data and shape the dataset as a supervised learning problem .5 Eki 2021

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 .