ARIMA can be applied to both one-step-ahead and multi-step-ahead forecasting.
- One-step-ahead prediction means that ARIMA predicts the next immediate value in a time series based on past data.
- Multi-step-ahead prediction refers to forecasting several steps into the future, though this tends to accumulate more error as predictions are iteratively based on previously forecasted values.
Tuning ARIMA model
- ARIMA (AutoRegressive Integrated Moving Average) models are inherently linear and for linear time series forecasting. They operate by combining past observations (autoregressive component), differencing to achieve stationarity (integrated component), and past errors (moving average component) to predict future values.
- The "autoregressive" (AR) part of ARIMA indicates that the evolving variable of interest is regressed (พัฒนาขึ้นมาจากขั้นก่อนหน้า) on its prior values. The "moving average" (MA) part indicates that the regression error is a linear combination of error terms whose values occurred contemporaneously and at various times in the past. The "integrated" (I) part indicates that the data values have been replaced with the difference between each value and the previous value.
