So far in this book, we have described traditional statistical methods for time series analysis. In the preceding chapters, we has discussed several methods to forecast the series at a future point in time from observations taken in the past. One such method to make predictions is the auto-regressive (AR) model, which expresses the series at time t as a linear regression of previous p observations:
Here, Єt is the residual error term from the AR model.
The idea underlying the linear model can be generalized that the objective of time series forecasting is to develop a function f that predicts x
