Time Series Forecasting in Python

5 Modeling an autoregressive process

This chapter covers

• Illustrating an autoregressive process
• Defining the partial autocorrelation function (PACF)
• Using the PACF plot to determine the order of an autoregressive process
• Forecasting a time series using the autoregressive model

In the previous chapter, we covered the moving average process, also denoted as MA(q)), where q is the order. You learned that in a moving average process, the present value is linearly dependent on current and past error terms. Therefore, if you predict more than q steps ahead, the prediction will fall flat and will return only the mean of the series, because the error terms are not observed in the data and must be recursively estimated. Finally, you saw that you can ...