The linear regression model, unlike the traditional time series models such as the ARIMA or Holt-Winters, was not designed explicitly to handle and forecast time series data. Instead, it is a generic model with a wide range of applications from causal inference to predictive analysis.

Therefore, forecasting with a linear regression model is mainly based on the following two steps:

1. Identifying the series structure, key characteristics, patterns, outliers, and other features
2. Transforming those features into input variables and regressing them with the series to create a forecasting model

The core features of a linear regression forecasting model are the trend and seasonal components. The next section focuses on identifying the series trend and seasonal components and then transforming them into input variables of the regression model.