ARIMA, which stands for AutoRegressive Integrated Moving Average, is a popular time series forecasting model. It combines autoregression (AR), differencing (I), and moving averages (MA) to capture patterns and trends in time series data. Here’s a brief explanation:
- AutoRegressive (AR): This component models the relationship between the current observation and its past values. The term “autoregressive” signifies that the model uses past observations as predictors for future values.
- Integrated (I): The differencing step involves transforming a non-stationary time series into a stationary one. Stationarity simplifies modeling as it assumes that statistical properties, such as mean and variance, remain constant over time.
- Moving Average (MA): This part captures the relationship between the current observation and a residual error from a moving average model applied to past observations.