Time Series Forecasting in Python

4 Modeling a moving average process

This chapter covers

• Defining a moving average process
• Using the ACF to identify the order of a moving average process
• Forecasting a time series using the moving average model

In the previous chapter, you learned how to identify and forecast a random walk process. We defined a random walk process as a series whose first difference is stationary with no autocorrelation. This means that plotting its ACF will show no significant coefficients after lag 0. However, it is possible that a stationary process may still exhibit autocorrelation. In this case, we have a time series that can be approximated by a moving average model MA(q)), an autoregressive model AR(p)), or an autoregressive moving average model ARMA( ...