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Multiresolution Data Analytics for Financial Time Series Using MATLAB

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Data Analytics for Management, Banking and Finance

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Abstract

In this chapter, we explore the use of multiresolution analysis techniques, including wavelet transforms such as the discrete wavelet transform (DWT), stationary wavelet transform (SWT), and empirical mode decomposition (EMD), for analyzing financial time series data in Matlab. These techniques allow for the decomposition of financial time series data into different frequency bands and the identification of trends and patterns at different scales, which can be useful for forecasting and trading strategies. We also explore the use of denoising techniques, such as wavelet thresholding, for improving the accuracy of financial time series data. Our results show that multiresolution analysis can provide valuable insights into financial time series data and can improve the performance of trading strategies.

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Author information

Authors and Affiliations

  1. Department of Business Computing, Institut Supérieur de Gestion de Sousse, Sousse, Tunisia

    Hana Rabbouch

  2. Department of Business Computing, École Pluridisciplinaire Internationale de Sousse, Sousse, Tunisia

    Bochra Rabbouch

  3. Department of Statistics, King Abdulaziz University, Jeddah, Saudi Arabia

    Foued Saâdaoui

Authors
  1. Hana Rabbouch
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  2. Bochra Rabbouch
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  3. Foued Saâdaoui
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Corresponding author

Correspondence to Hana Rabbouch .

Editor information

Editors and Affiliations

  1. Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia

    Foued Saâdaoui

  2. Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, USA

    Yichuan Zhao

  3. Department of Business Computing, University of Sousse, Sousse, Tunisia

    Hana Rabbouch

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Rabbouch, H., Rabbouch, B., Saâdaoui, F. (2023). Multiresolution Data Analytics for Financial Time Series Using MATLAB. In: Saâdaoui, F., Zhao, Y., Rabbouch, H. (eds) Data Analytics for Management, Banking and Finance. Springer, Cham. https://doi.org/10.1007/978-3-031-36570-6_5

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