Performance of combined double seasonal univariate time series models for forecasting water demand
AbstractIn 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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Bibliographic InfoPaper 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.
Length: 12 pages
Date of creation: May 2009
Date of revision:
ARIMA; Combined forecasts; Double seasonality; Exponential Smoothing; Forecasting; GARCH; Water demand;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-05-16 (All new papers)
- NEP-ECM-2009-05-16 (Econometrics)
- NEP-ETS-2009-05-16 (Econometric Time Series)
- NEP-FOR-2009-05-16 (Forecasting)
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