Multiple linear-regression models expressed the variable of interest as a linear combination of predictors or input variables. Univariate time series models relate the value of the time series at the point in time of interest to a linear combination of lagged values of the series and possibly past disturbance terms.
While exponential smoothing models are based on a description of the trend and seasonality in the data, ARIMA models aim to describe the autocorrelations in the data. ARIMA(p, d, q) models require stationarity and leverage two building blocks:
- Autoregressive (AR) terms consisting of p-lagged values of the time series
- Moving average (MA) terms that contain q-lagged disturbances
The I stands for integrated because the model can account for unit-root non-stationarity by differentiating the series d times. The term autoregression underlines...
