Multiresolution forecasting for futures trading using wavelet decompositions
| Autor: | D. R. Dersch, Richard Coggins, Bailing Zhang, Marwan A. Jabri, Barry Flower |
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| Rok vydání: | 2008 |
| Předmět: |
Artificial neural network
Computer Networks and Communications Computer science business.industry Autocorrelation Wavelet transform General Medicine Machine learning computer.software_genre Perceptron Computer Science Applications Wavelet Artificial Intelligence Multilayer perceptron Artificial intelligence Invariant (mathematics) business Algorithm computer Software |
| Zdroj: | IEEE transactions on neural networks. 12(4) |
| ISSN: | 1045-9227 |
| Popis: | We investigate the effectiveness of a financial time-series forecasting strategy which exploits the multiresolution property of the wavelet transform. A financial series is decomposed into an over complete, shift invariant scale-related representation. In transform space, each individual wavelet series is modeled by a separate multilayer perceptron (MLP). We apply the Bayesian method of automatic relevance determination to choose short past windows (short-term history) for the inputs to the MLPs at lower scales and long past windows (long-term history) at higher scales. To form the overall forecast, the individual forecasts are then recombined by the linear reconstruction property of the inverse transform with the chosen autocorrelation shell representation, or by another perceptron which learns the weight of each scale in the prediction of the original time series. The forecast results are then passed to a money management system to generate trades. |
| Databáze: | OpenAIRE |
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