ARIMA
Autoregressive Integrated Moving Average is a popular class of forecasting models for time series data.
What is ARIMA?
ARIMA models predict future values based entirely on the series' own past values. It combines three components: Autoregression (AR), which uses past values; Integration (I), which involves differencing the data to achieve stationarity; and Moving Average (MA), which models the relationship between an observation and a residual error from previous steps.
Why ARIMA Matters
It provides a robust, flexible framework for forecasting univariate time series data that exhibit trends or autocorrelated structures, without needing explanatory variables.
Example
A supply chain analyst uses an ARIMA(1,1,1) model to forecast next month's product demand based solely on the historical monthly sales data.
Common Mistakes
- Applying ARIMA to non-stationary data without determining the correct differencing order (Integration parameter).
- Using ARIMA for long-term forecasting, as its predictions quickly revert to the mean or trend line.