Combining the wavelet transform and forecasting models to predict gas forward prices

Hang T. Nguyen; Ian T. Nabney

doi:10.1109/ICMLA.2008.37

Combining the wavelet transform and forecasting models to predict gas forward prices

Hang T. Nguyen
, Ian T. Nabney

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a book

10 Citations (Scopus)

11 Downloads (Pure)

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.

Original language	English
Title of host publication	Seventh International Conference on Machine Learning and Applications, 2008. ICMLA '08
Place of Publication	United States
Publisher	IEEE Computer Society
Pages	311-317
Number of pages	7
ISBN (Print)	9780769534954
DOIs	https://doi.org/10.1109/ICMLA.2008.37
Publication status	Published - 1 Dec 2008

Bibliographical note

Seventh International Conference on Machine Learning and Applications, San Diego (US)

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, GARCH, linear regression, multi-layer perceptron

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title = "Combining the wavelet transform and forecasting models to predict gas forward prices",

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.",

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PY - 2008/12/1

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N2 - 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.

AB - 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.

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DO - 10.1109/ICMLA.2008.37

M3 - Chapter in a book

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BT - Seventh International Conference on Machine Learning and Applications, 2008. ICMLA '08

PB - IEEE Computer Society

CY - United States

ER -

Combining the wavelet transform and forecasting models to predict gas forward prices

Abstract

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