Time Series Forecasting in Python

6 Modeling complex time series

This chapter covers

• Examining the autoregressive moving average model or ARMA(p,q)
• Experimenting with the limitations of the ACF and PACF plots
• Selecting the best model with the Akaike information criterion (AIC)
• Analyzing a time series model using residual analysis
• Building a general modeling procedure
• Forecasting using the ARMA(p,q) model

In chapter 4 we covered the moving average process, denoted as MA(q)), where q is the order. You learned that in a moving average process, the present value is linearly dependent on the mean, the current error term, and past error terms. The order q can be inferred using the ACF plot, where autocorrelation coefficients will be significant up until lag q only. In the case where ...