DocumentCode :
2006783
Title :
Combining the Wavelet Transform and Forecasting Models to Predict Gas Forward Prices
Author :
Nguyen, Hang T. ; Nabney, Ian T.
Author_Institution :
Neural Comput. Res. Group, Aston Univ., Birmingham, UK
fYear :
2008
fDate :
11-13 Dec. 2008
Firstpage :
311
Lastpage :
317
Abstract :
This paper presents a forecasting technique for forward energy prices, one day ahead. This technique combines a wavelet transform and forecasting models such as multi-layer perceptron, linear regression or GARCH. These techniques are applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the wavelet transform. The methodology can be also applied to forecasting market clearing prices and electricity/gas loads.
Keywords :
forecasting theory; natural gas technology; power markets; power system economics; pricing; wavelet transforms; UK gas market; electricity load; forecasting model; forward energy price; gas forward price prediction; gas load; market clearing price forecasting; wavelet transform; Demand forecasting; Economic forecasting; Forward contracts; Input variables; Linear regression; Load forecasting; Multilayer perceptrons; Neural networks; Predictive models; Wavelet transforms; GARCH; linear regression; multi-layer perceptron; wavelet transform;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications, 2008. ICMLA '08. Seventh International Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
978-0-7695-3495-4
Type :
conf
DOI :
10.1109/ICMLA.2008.37
Filename :
4724991
Link To Document :
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