Regression Extension Techniques for Time-Series Data

Abstract

In this chapter, you will learn how to leverage regression techniques to solve time-series problems efficiently. Regression is a supervised learning technique in machine learning where you try to estimate target variables using one or multiple regressors. In this chapter, you will learn what stationary means in time-series data as well as how to make data stationery, how to interpret p-values, and how to test whether a series is stationary. Finally, we’ll explore the AR, MA, ARIMA, SARIMA, SARIMAX, VAR, and VARMA techniques and solve some problems with real-world datasets.