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

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Author Info
Jorge Caiado () (CEMAPRE, School of Economics and Management (ISEG), Technical University of Lisbon)

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Abstract

In this article, we examine the daily water demand forecasting performance of double seasonal univariate time series models (Holt-Winters, ARIMA and GARCH) based on multi-step 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. We investigate whether combining forecasts from different methods for different origins and horizons could improve forecast accuracy. The analysis is made with daily data for water consumption in Granada, Spain.

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Publisher Info
Paper provided by Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon in its series CEMAPRE Working Papers with number 0903.

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Length: 12 pages
Date of creation: May 2009
Date of revision:
Handle: RePEc:cma:wpaper:0903

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Related research
Keywords: ARIMA; Combined forecasts; Double seasonality; Exponential Smoothing; Forecasting; GARCH; Water demand;

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April. [Downloadable!] (restricted)
  2. Sean D. Campbell & Francis X. Diebold, 2005. "Weather Forecasting for Weather Derivatives," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 6-16, March. [Downloadable!] (restricted)
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  3. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July. [Downloadable!] (restricted)
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This page was last updated on 2009-11-24.

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