Combining the Wavelet Transform and Forecasting Models to Predict Gas Forward Prices

  • Authors:

  • Hang T. Nguyen;Ian T. Nabney

  • Affiliations:

  • -;-

  • Venue:
  • ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

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.