Plotting ACF and PACF
Before building any statistical forecasting models such as AR (AutoRegressive), MA (Moving Average), ARMA (AutoRegressive Moving Average), ARIMA (AutoRegressive Integrated Moving Average), or SARIMA (Seasonal AutoRegressive Integrated Moving Average), you will need to determine the most suitable type of time series model for your data. Additionally, you will need to identify the values for some required parameters, known as orders. More specifically, these include the lag orders for the autoregressive (AR) or moving average (MA) components. This process will be explored further in the 'Forecasting Univariate Time Series Data with ARIMA' section of this chapter. For example, an Autoregressive Moving Average (ARMA) model is denoted as ARMA(p, q), where 'p' represents the autoregressive order, or AR(p) component, and 'q' represents the moving average order, or MA(q) component. Hence, an ARMA model combines an AR(p) and an MA(q) model.
