24-Hours Demand Forecasting Based on SARIMA and Support Vector Machines

In time series analysis the autoregressive integrate moving average (ARIMA) models have been used for decades and in a wide variety of scientific applications. In recent years a growing popularity of machine learning algorithms like the artificial neural network (ANN) and support vector machine (SVM...

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Bibliographic Details
Published in:Procedia engineering Vol. 89; pp. 926 - 933
Main Authors: Braun, M., Bernard, T., Piller, O., Sedehizade, F.
Format: Journal Article
Language:English
Published: Elsevier Ltd 2014
Elsevier
Subjects:
calibration
Engineering Sciences
forecasting
Machine Learning
Mathematics
prediction
SARIMA
Statistics
support vector machine
water demand
water distribution network
water demand
support vector machine
water distribution network
SARIMA
forecasting
prediction
calibration
RESEAU DE DISTRIBUTION D'EAU
DEMANDE EN EAU
MODEL CALIBRATION
WATER DISTRIBUTION SYSTEM
CALAGE DE MODELE
PREDICTION
ISSN:1877-7058, 1877-7058
Online Access:Get full text
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Summary:In time series analysis the autoregressive integrate moving average (ARIMA) models have been used for decades and in a wide variety of scientific applications. In recent years a growing popularity of machine learning algorithms like the artificial neural network (ANN) and support vector machine (SVM) have led to new approaches in time series analysis. The forecasting model presented in this paper combines an autoregressive approach with a regression model respecting additional parameters. Two modelling approaches are presented which are based on seasonal autoregressive integrated moving average (SARIMA) models and support vector regression (SVR). These models are evaluated on data from a residential district in Berlin.
ISSN:1877-7058
1877-7058
DOI:10.1016/j.proeng.2014.11.526