# Chapter 5 Linear regression models

In this chapter we discuss linear regression models. The basic concept is that we forecast the time series of interest $$y$$ assuming that it has a linear relationship with other time series $$x$$.

For example, we might wish to forecast monthly sales $$y$$ with total advertising spend $$x$$ as the predictor. Or we might forecast daily electricity demand $$y$$ using temperature $$x_1$$ and the day of week $$x_2$$ as predictors.

The forecast variable $$y$$ is sometimes also called the regressand, dependent or explained variable. The predictor variables $$x$$ are sometimes also called the regressors, independent or explanatory variables. In this book we will always refer to them as the “forecast variable” and “predictor variables”.