Forecasting with ARIMA Models

The Autoregressive Integrated Moving Average (ARIMA) model is the generic name for a family of forecasting models that are based on the Autoregressive (AR) and Moving Average (MA) processes. Among the traditional forecasting models (for example, linear regression, exponential smoothing, and so on), the ARIMA model is considered as the most advanced and robust approach. In this chapter, we will introduce the model components—the AR and MA processes and the differencing component. Furthermore, we will focus on methods and approaches for tuning the model's parameters with the use of differencing, the autocorrelation function (ACF), and the partial autocorrelation function (PACF).

In this chapter, we will cover the following topics:

• The stationary state of time series data
• The random walk process
• The AR and MA processes
• The ARMA and ARIMA...