Autoregressive Integrated Moving Average (ARIMA) Model
What is Autoregressive Integrated Moving Average (ARIMA) Model?
An autoregressive integrated moving average (ARIMA) model is a statistical model used for forecasting time series data. It is a combination of two models: an autoregressive (AR) model and a moving average (MA) model.
An autoregressive model is a statistical model that uses past values of a time series to forecast future values. An MA model, on the other hand, uses the error terms (the difference between the actual value and the predicted value) from past predictions to forecast future values.
The "integrated" part of the ARIMA model refers to the fact that the time series data may need to be "differenced" (i.e., the difference between consecutive observations is taken) in order to make it stationary (i.e., to remove any trend or seasonal component).
ARIMA models are widely used in economics, finance, and other fields to analyze and forecast time series data. They are particularly useful for forecasting data with a long-term trend or seasonal component.