Overview
ARIMA stands for AutoRegressive Integrated Moving Average. It is a powerful and flexible model that combines three components to handle different aspects of time series data.
Components
- AR (Autoregression): Uses the relationship between an observation and a number of lagged observations.
- I (Integrated): Uses differencing of raw observations to make the time series stationary.
- MA (Moving Average): Uses the dependency between an observation and a residual error from a moving average model applied to lagged observations.
Parameters
Represented as ARIMA(p, d, q), where p is the number of lag observations, d is the degree of differencing, and q is the size of the moving average window.