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

Caiado, Jorge (2009): Performance of combined double seasonal univariate time series models for forecasting water consumption.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: ARIMA; Combined forecasts; Double seasonality; Exponential Smoothing; Forecasting; GARCH; Water demand.
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes

References

Armstrong, J. (2001). "Combining forecasts", in Principles of Forecasting: A Handbook for Researchers and Practitioners, J. S. Armstrong (ed.), Kluwer Academic Publishers.

Bollerslev, T. (1986). "Generalized autoregressive conditional heteroskedasticity", Journal of Econometrics, 31, 307-327.

Bollerslev, T., Chou, R. and Kroner, K. (1992). "ARCH modeling in Finance", Journal of Econometrics, 52, 5-59.

Bougadis, J., Adamowski, K. and Diduch, R. (2005). "Short-term municipal water deamand forecasting", Hydrological Processes, 19, 137-148.

Box, G., Jenkins, G. and Reinsel, G. (1994). Time Series Analysis: Forecasting and Control, 3rd ed., Prentice-Hall, New Jersey.

Campbell, S. and Diebold, F. (2005). "Weather forecasting for weather derivatives", Journal of the American Statistical Association, 100, 6-16.

Clemen, R. (1989). "Combining forecasts: a review and annoted bibliography", International Journal of Forecasting, 5, 559-584.

Davison, R. and MacKinnon, J. (1993). Estimation and Inference in Econometrics, Oxford University Press, Oxford.

Engle, R. (1982). "Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation", Econometrica, 50, 987-1008.

Fildes, R., Randall, A. and Stubbs, P. (1997). "One-day ahead demand forecasting in the utility industries: Two case studies", Journal of the Operational Research Society, 48, 15-24.

Gardner Jr., E. (2006). "Exponential smoothing: The state of the art - Part II", International Journal of Forecasting, 22, 637-666.

Gato, S., Jayasuriya, N. and Roberts, P.(2007). "Temperature and rainfall thresholds for base use urban water demand modelling", Journal of Hydrology, 337, 364-376.

Jain, A., Varshney, A. and Joshi, U. (2001). "Short-term water demand forecast modeling at IIT Kanpur using artificial neural networks", Water Resources Management, 15, 299-231.

Maidment, D. and Miaou, S. (1986). "Daily water use in nine cities", Water Resources Research, 22, 845-851.

Nelson, D. (1991). "Conditional heteroskedasticity in asset returns: a new approach", Econometrica, 59, 347-370.

Taylor, J. (2003). "Short-term electricity demand forecasting using double seasonal exponential smoothing", Journal of the Operational Research Society, 54, 799-805.

Taylor, J. (2006). "Density forecasting for the efficient balancing of the generation and consumption of electricity", International Journal of Forecasting, 22, 707-724.

Zhou, S., McMahon, T., Walton, A. and Lewis, J. (2000). "Forecasting daily urban water demand: a case study of Melbourne", Journal of Hidrology, 236, 153-164.