Plotting ACF and PACF
When building statistical forecasting models such as AR, MA, ARMA, ARIMA, or SARIMA, you will need to determine the type of time series model that is most suitable for your data and the values for some of the required parameters, called orders. More specifically, these are called the lag orders for the autoregressive (AR) or moving average (MA) components. This will be explored further in the Forecasting univariate time series data with non-seasonal ARIMA recipe of this chapter.
To demonstrate this, for example, an Autoregressive Moving Average (ARMA) model can be written as ARMA(p, q), where p is the autoregressive order or AR(p) component, and q is the moving average order or MA(q) component. Hence, an ARMA model combines an AR(p) and an MA(q) model.
The core idea behind these models is built on the assumption that the current value of a particular variable, 
, can be estimated from past values of itself. For example, in an autoregressive model of order...
