# Chapter 6 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.