Integration of multi-time-scale models in time series forecasting

Abstract

A solution to the problem of producing long-range forecasts on a short sampling interval is proposed. It involves the incorporation of information from a long sampling interval series, which could come from an independent source, into forecasts produced by a state-space model based on a short sampling interval. The solution is motivated by the desire to incorporate yearly electricity consumption information into weekly electricity consumption forecasts. The weekly electricity consumption forecasts are produced by a state-space structural time series model. It is shown that the forecasts produced by the forecasting model based on weekly data can be improved by the incorporation of longer-time-scale information, particularly when the forecast horizon is increased from 1 year to 3 years. A further example is used to demonstrate the approach, where yearly UK primary fuel consumption information is incorporated into quarterly fuel consumption forecasts.