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Integration of multi-time-scale models in time series forecasting
Murray, Fiona T.; Ringwood, John; Austin, Paul C.
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-tim e-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.
Keyword(s): multi-time-scale models; time series forecasting; long range forecasts; electricity consumption forecasts
Publication Date:
2000
Type: Journal article
Peer-Reviewed: Yes
Institution: Maynooth University
Citation(s): Murray, Fiona T. and Ringwood, John and Austin, Paul C. (2000) Integration of multi-time-scale models in time series forecasting. International Journal of Systems Science, 31 (10). pp. 1249-1260. ISSN 0020-7721
Publisher(s): Taylor & Francis
File Format(s): other
Related Link(s): http://eprints.maynoothuniversity.ie/9511/1/JR-Integration-2000.pdf
First Indexed: 2018-06-07 06:05:54 Last Updated: 2018-06-07 06:05:54