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Forecasting water consumption in Spain using univariate time series models

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: C22 C32 (search for similar items in EconPapers)
Date: 2007-09
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Published in Proceedings of IEEE Spanish Computational Intelligence Society (2007): pp. 415-423

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https://mpra.ub.uni-muenchen.de/6610/1/MPRA_paper_6610.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/15242/2/MPRA_paper_15242.pdf revised version (application/pdf)

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