# Chapter 5 Time series regression models

In this chapter we discuss 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 using total advertising spend x as a 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.