Caiado, Jorge (2007): Forecasting water consumption in Spain using univariate time series models. Published in: Proceedings of IEEE Spanish Computational Intelligence Society (September 2007): pp. 415-423.
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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|
|Original Title:||Forecasting water consumption in Spain using univariate time series models|
|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
|Depositing User:||Jorge Caiado|
|Date Deposited:||07. Jan 2008 04:34|
|Last Modified:||15. Feb 2013 01:26|
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