10.3: Moving Average (MA) Models
Since AR models only look back over a finite number of samples, they need time to adjust to unexpected shocks in a time series. You must model past instances of the input noise to handle unforeseen shocks. Moving average (MA) models can be used for this purpose. Read this article to learn the general structure of the MA model.
This tutorial provides several examples of MA models of various orders. In addition, the partial autocorrelation (PACF) function is introduced. The ACF and PACF are important tools for estimating the order of a model based on empirical data.
This video summarizes the key points regarding AR and MA models. In general, stationary time series modeling requires a balance between these two approaches. In the next section, you will learn how to combine them and apply them in time series analysis.