Performance of combined double seasonal univariate time series models for forecasting water consumption

Jorge Caiado ()

MPRA Paper from University Library of Munich, Germany

Abstract: In this paper, we examine the daily water demand forecasting performance of double seasonal univariate time series models (Exponential Smoothing, ARIMA and GARCH) based on multi-step ahead forecast mean squared errors. We investigate whether combining forecasts from different methods and from different origins and horizons could improve forecast accuracy. We use daily data for water consumption in Spain from 1 January 2001 to 30 June 2006.

Keywords: ARIMA; Combined forecasts; Double seasonality; Exponential Smoothing; Forecasting; GARCH; Water demand. (search for similar items in EconPapers)
JEL-codes: C32 C22 (search for similar items in EconPapers)
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Date: 2009-01
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http://mpra.ub.uni-muenchen.de/6610/ orginal version
http://mpra.ub.uni-muenchen.de/15242/ revised version

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