Hands-On Machine Learning for Algorithmic Trading

How to identify the number of AR and MA terms

Since AR(p) and MA(q) terms interact, the information provided by the ACF and PACF is no longer reliable and can only be used as a starting point.

Traditionally, the AIC and BIC information criteria have been used to rely on in-sample fit when selecting the model design. Alternatively, we can rely on out-of-sample tests to cross-validate multiple parameter choices.

The following summary provides some generic guidance to choose the model order in the case of considering AR and MA models in isolation:

• The lag beyond which the PACF cuts off is the indicated number of AR terms. If the PACF of the differenced series cuts off sharply and/or the lag-1 autocorrelation is positive, add one or more AR ...