midTime Series
What is ARIMA and what are its components?
Updated May 15, 2026
Short answer
ARIMA is a forecasting model combining AutoRegressive, Integrated (differencing), and Moving Average components.
Deep explanation
ARIMA models time series using three components: AR (p) models dependency on past values, I (d) represents differencing to ensure stationarity, and MA (q) models dependency on past forecast errors. Together, they capture both temporal dependence and noise structure. ARIMA assumes linear relationships and works best on stationary or transformed series.
Real-world example
Forecasting monthly electricity consumption using historical usage patterns.
Common mistakes
- Using ARIMA on non-stationary data without differencing.
Follow-up questions
- What does AR(p) mean?
- What is MA(q)?