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Related Experiment Videos

Multiresolution forecasting for futures trading using wavelet decompositions.

B L Zhang1, R Coggins, M A Jabri

  • 1Computer Engineering Laboratory, School of Electrical and Information Engineering, University of Sydney, NSW 2006, Australia.

IEEE Transactions on Neural Networks
|February 6, 2008
PubMed
Summary
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This study introduces a novel financial forecasting strategy using wavelet transforms and multilayer perceptrons (MLPs). The method effectively predicts time-series data by analyzing financial series at multiple resolutions, improving trading strategies.

Area of Science:

  • * Computational Finance
  • * Signal Processing
  • * Machine Learning

Background:

  • * Accurate financial time-series forecasting remains a significant challenge.
  • * Traditional methods often struggle to capture complex, multiresolution dynamics.
  • * Wavelet transform offers a powerful tool for analyzing data at different scales.

Purpose of the Study:

  • * To evaluate a novel financial forecasting strategy leveraging wavelet transform.
  • * To model individual wavelet series using multilayer perceptrons (MLPs).
  • * To optimize input window selection for MLPs using Bayesian methods.

Main Methods:

  • * Decomposition of financial series into a shift-invariant, multiresolution representation using wavelet transform.

Related Experiment Videos

  • * Modeling of each wavelet series with separate MLPs.
  • * Application of Bayesian automatic relevance determination for adaptive input window selection (short-term for low scales, long-term for high scales).
  • * Recombination of individual forecasts via inverse transform or a meta-perceptron.

    • Main Results:

    • * The proposed strategy effectively forecasts financial time-series data.
    • * Adaptive input window selection enhances MLP performance across different scales.
    • * The integrated approach, including a money management system, generates profitable trades.

    Conclusions:

    • * The wavelet transform-based MLP strategy provides a robust framework for financial forecasting.
    • * Multiresolution analysis combined with adaptive learning improves prediction accuracy.
    • * The methodology demonstrates practical applicability in automated trading systems.