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

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  • Jorge Caiado

    (CEMAPRE, School of Economics and Management (ISEG), Technical University of Lisbon)

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.

Suggested Citation

  • Jorge Caiado, 2009. "Performance of combined double seasonal univariate time series models for forecasting water demand," CEMAPRE Working Papers 0903, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.
    • Handle: RePEc:cma:wpaper:0903
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    References listed on IDEAS

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    1. 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.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    4. 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.
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    Cited by:

    1. Rafael Benítez & Carmen Ortiz-Caraballo & Juan Carlos Preciado & José M. Conejero & Fernando Sánchez Figueroa & Alvaro Rubio-Largo, 2019. "A Short-Term Data Based Water Consumption Prediction Approach," Energies, MDPI, vol. 12(12), pages 1-24, June.
    2. Xiao-jun Wang & Jian-yun Zhang & Shahid Shamsuddin & Ru-lin Oyang & Tie-sheng Guan & Jian-guo Xue & Xu Zhang, 2017. "Impacts of climate variability and changes on domestic water use in the Yellow River Basin of China," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 22(4), pages 595-608, April.
    3. E. Pacchin & F. Gagliardi & S. Alvisi & M. Franchini, 2019. "A Comparison of Short-Term Water Demand Forecasting Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1481-1497, March.
    4. Xiao-jun Wang & Jian-yun Zhang & Shamsuddin Shahid & En-hong Guan & Yong-xiang Wu & Juan Gao & Rui-min He, 2016. "Adaptation to climate change impacts on water demand," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 21(1), pages 81-99, January.
    5. Xiao-Jun Wang & Jian-Yun Zhang & Shamsuddin Shahid & Wei Xie & Chao-Yang Du & Xiao-Chuan Shang & Xu Zhang, 2018. "Modeling domestic water demand in Huaihe River Basin of China under climate change and population dynamics," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(2), pages 911-924, April.
    6. Jens Kley-Holsteg & Florian Ziel, 2020. "Probabilistic Multi-Step-Ahead Short-Term Water Demand Forecasting with Lasso," Papers 2005.04522, arXiv.org.

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