Abstract

Dynamic linear regression models forecast the values of one time series based on a linear combination of a set of exogenous time series, while incorporating a time series process for the error term. This error process is often assumed to follow an autoregressive integrated moving average (ARIMA) model, or seasonal variants thereof, which are unable to capture a long-range dependency structure of the error process. We propose a novel dynamic linear regression model that incorporates such dependency and show that it improves the model's forecasting ability. We illustrate the model's superior predictive accuracy in two energy forecasting applications.