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

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.

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Year of publication:	2009-01
Authors:	Caiado, Jorge

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Type of publication:	Book / Working Paper
Language:	English
Notes:	Caiado, Jorge (2009): Performance of combined double seasonal univariate time series models for forecasting water consumption.
Classification:	C32 - Time-Series Models ; C22 - Time-Series Models
Source:	BASE

Persistent link: https://www.econbiz.de/10015216874