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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Veröffentlicht in:	Procedia engineering Jg. 89; S. 926 - 933
Hauptverfasser:	Braun, M., Bernard, T., Piller, O., Sedehizade, F.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 2014 Elsevier
Schlagworte:	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-Zugang:	Volltext
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Beschreibung
Zusammenfassung:	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