On neuro-wavelet modeling

Authors:
F. Murtagh;J. L. Starck;O. Renaud
Affiliations:
School of Computer Science, Queen's University Belfast, 18 Malone Road, Belfast BT7 1NN, Northern Ireland, UK;DAPNIA/SEI-SAP, CEA-Saclay, 91191 Gif-sur-Yvette, France;Faculté de Psychologie et Sciences de l'Education, 40 Bd. du Pont d'Arve, 1211 Geneva 4, Switzerland
Venue:
Decision Support Systems - Special issue: Data mining for financial decision making
Year:
2004

Citing 6
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Abstract

We survey a number of applications of the wavelet transform in time series prediction. The Haar à trous wavelet transform is proposed as a means of handling time series data when future data is unknown. Results are exemplified on financial futures and S&P500 data. Nonlinear and linear multiresolution autoregressionmodels are studied. Experimentally, we show that multiresolution approaches can outperform the traditional single resolution approach to modeling and prediction.