The linear regression model is one of the most common methods for identifying and quantifying the relationship between a dependent variable and a single (univariate linear regression) or multiple (multivariate linear regression) independent variables. This model has a wide range of applications, from causal inference to predictive analysis and, in particular, time series forecasting.
The focus of this chapter is on methods and approaches for forecasting time series data with linear regression. That includes methods for decomposing and forecasting the series components (for example, the trend and seasonal patterns), handling special events (such as outliers and holidays), and using external variables as regressors.
This chapter covers the following topics:
- Forecasting approaches with linear regression models
- Extracting and estimating the series...
