Performance of Combined Double Seasonal Univariate Time Series Models for Forecasting Water Demand
Publication: Journal of Hydrologic Engineering
Volume 15, Issue 3
Abstract
This paper examines the daily water demand forecasting performance of double seasonal univariate time series models (Holt-Winters, ARIMA, and GARCH) based on multistep ahead forecast mean squared errors. A within-week seasonal cycle and a within-year seasonal cycle are accommodated in the various model specifications to capture both seasonalities. The study investigates whether combining forecasts from different methods could improve forecast accuracy. The results suggest that the combined forecasts perform quite well, especially for short-term forecasting. On the other hand, the individual forecasts from Holt-Winters exponential smoothing and GARCH models can improve forecast accuracy on specific days of the week.
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Acknowledgments
The writer acknowledges the helpful comments of two anonymous referees and the participants at the Spanish IEEE Computational Intelligence Society SICO'2007 Conference. This research was supported by a grant from the Fundação para a Ciência e Tecnologia (FEDER/POCI 2010).
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Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 15 • Issue 3 • March 2010
Pages: 215 - 222
Copyright
© 2010 ASCE.
History
Received: Feb 20, 2009
Accepted: Sep 17, 2009
Published online: Sep 18, 2009
Published in print: Mar 2010
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