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Financial time series forecasting using optimized multistage wavelet regression approach.

P Syamala Rao1, G Parthasaradhi Varma2, Ch Durga Prasad3

  • 1Department of CSE, Acharya Nagarjuna University, Guntur, India.

International Journal of Information Technology : an Official Journal of Bharati Vidyapeeth'S Institute of Computer Applications and Management
|May 2, 2022
PubMed
Summary

This study forecasts financial time series using multistage wavelet transform (WT) and particle swarm optimization (PSO). The research optimizes regression models for accurate future sample prediction, aiding data scientists in parameter selection.

Keywords:
Linear regressionParticle swarm optimizationWavelet transform

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Area of Science:

  • Computational Finance
  • Signal Processing
  • Machine Learning

Background:

  • Financial time series forecasting is crucial for economic stability and investment strategies.
  • Traditional forecasting methods often struggle with the complex, noisy nature of financial data.
  • Wavelet Transform (WT) and Particle Swarm Optimization (PSO) are advanced techniques with potential for improved time series analysis.

Purpose of the Study:

  • To present a novel approach for financial time series forecasting using a combination of multistage wavelet transform and particle swarm optimization.
  • To investigate the impact of different mother wavelet functions and decomposition levels on prediction accuracy.
  • To analyze the influence of PSO control parameters on the optimization of regression models for time series prediction.

Main Methods:

  • Financial time series data was decomposed using multistage wavelet transform (WT) with various mother wavelets to extract high and low-frequency coefficients.
  • Particle Swarm Optimization (PSO) was employed to identify optimal regression model coefficients, minimizing Mean Square Error (MSE).
  • The study explored different WT decomposition levels and PSO control parameters to assess their effect on forecasting performance.

Main Results:

  • The multistage WT effectively extracted relevant features from financial time series data.
  • PSO successfully optimized regression models, leading to improved prediction of future financial samples.
  • Varying mother wavelets and decomposition levels significantly impacted the accuracy of time series predictions.
  • PSO control parameters were found to be critical for efficient search and model optimization.

Conclusions:

  • The integrated approach of multistage WT and PSO offers a robust framework for financial time series forecasting.
  • Data scientists can leverage these findings to select optimal wavelet functions, decomposition levels, and PSO parameters for enhanced prediction accuracy.
  • This research contributes to the development of more reliable tools for financial market analysis and prediction.