Caiado, Jorge (2009): Performance of combined double seasonal univariate time series models for forecasting water consumption.
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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:||Performance of combined double seasonal univariate time series models for forecasting water consumption|
|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
C - Mathematical and Quantitative Methods > C2 - Single Equation Models; Single Variables > C22 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
|Depositing User:||Jorge Caiado|
|Date Deposited:||23. May 2009 17:57|
|Last Modified:||09. Jan 2014 05:40|
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Forecasting water consumption in Spain using univariate time series models. (deposited 07. Jan 2008 04:34)
- Performance of combined double seasonal univariate time series models for forecasting water consumption. (deposited 23. May 2009 17:57) [Currently Displayed]