In the following sections, we will introduce the Autoregressive Integrated Moving Average (ARIMA), the most traditional type of forecasting model. We will also introduce a neural network model. ARIMA is a class of statistical models that is used to forecast a time series using past values. ARIMA is an acronym for the following:
- AR (autoregression): Autoregression is a process that takes previous data values as inputs, applies this to the regression equation, and generates resultant prediction-based data values.
- I (integrated): ARIMA uses an integrated approach by using differences in observations to make the time series equally spaced. This is done by subtracting the observation from an observation on a previous step or time value.
- MA (moving average): A model that uses the observation and the residual error applied to past observations.