In Chapter 9, Forecasting with Linear Regression, we saw that with some simple steps, we can utilize a linear regression model as a time series forecasting model. Recall that a general form of the linear regression model can be represented by the following equation:
One of the main assumptions of the linear regression model is that the error term of the series, 
, is the white noise series (for example, there is no correlation between the residuals and their lags). However, when working with time series data, this assumption is eased as, typically, the model predictors do not explain all the variations of the series, and some patterns are left on the model residuals. An example of the failure of this assumption can be seen while fitting a linear regression model to forecast the AirPassenger series.
