A prediction method for time series based on wavelet neural networks

Authors:
Xiaobing Gan;Ying Liu;Francis R. Austin
Affiliations:
College of Management, Shenzhen University, Shenzhen, China;China Academy of Urban Planning & Design, Shenzhen, China;College of Science, Shenzhen University, Shenzhen, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

Citing 2
Cited 0

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Abstract

This paper introduces a prediction method for time series that is based on the multi-resolution analysis of wavelets (MRA). The MRA is better able to decompose the non-stationary time series of nonlinear systems into different components, allowing a better separation of the general trend terms, the periodic terms and the random fluctuation terms. By applying the most suitable prediction methods(for example, the neural networks method, cosine approximation, or the ARMA model) to the components under different resolutions, this new prediction method produces more accurate prediction results. The new approach is then applied to a real example – the BRENT oil price time series – to demonstrates its usefulness and validity.