Munich Personal RePEc Archive

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.

This is the latest version of this item.

[img]
Preview
PDF
MPRA_paper_15242.pdf

Download (174kB) | Preview

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.

Available Versions of this Item

UB_LMU-Logo
MPRA is a RePEc service hosted by
the Munich University Library in Germany.