ARIMA Time Series Model

Overview

ARIMA(p,d,q) combines autoregression (AR), differencing (I), and moving average (MA) for time series forecasting. Seasonal variant: SARIMA(p,d,q)(P,D,Q,s). Requires stationary data (achieved through differencing). Best for univariate series with clear trend/seasonality patterns.

When to Use

Trigger conditions:

• Forecasting univariate time series (sales, demand, traffic)
• Data has clear trend and/or seasonal patterns
• Need interpretable model with statistical properties

When NOT to use:

• For multivariate forecasting with many external features (use ML models)
• For very long-range forecasts (ARIMA confidence intervals widen rapidly)
• For irregular/event-driven data (use causal models)

Algorithm