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

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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-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.

Item Type:	Article
Keywords:	multi-time-scale models; time series forecasting; long range forecasts; electricity consumption forecasts;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering
Item ID:	9511
Identification Number:	doi.org/10.1080/00207720050165753
Depositing User:	Professor John Ringwood
Date Deposited:	06 Jun 2018 13:58
Journal or Publication Title:	International Journal of Systems Science
Publisher:	Taylor & Francis
Refereed:	Yes
URI:	https://www.tandfonline.com
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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