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 proceedingChapter in a book

8 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 languageEnglish
Title of host publicationSeventh International Conference on Machine Learning and Applications, 2008. ICMLA '08
Place of PublicationUnited States
PublisherIEEE Computer Society
Pages311-317
Number of pages7
ISBN (Print)9780769534954
DOIs
Publication statusPublished - 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

Fingerprint Dive into the research topics of 'Combining the wavelet transform and forecasting models to predict gas forward prices'. Together they form a unique fingerprint.

  • Cite this

    Nguyen, H. T., & Nabney, I. T. (2008). Combining the wavelet transform and forecasting models to predict gas forward prices. In Seventh International Conference on Machine Learning and Applications, 2008. ICMLA '08 (pp. 311-317). IEEE Computer Society. https://doi.org/10.1109/ICMLA.2008.37