Multiresolution Data Analytics for Financial Time Series Using MATLAB

Hana Rabbouch (), Bochra Rabbouch and Foued Saâdaoui ()
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Hana Rabbouch: Institut Supérieur de Gestion de Sousse, Department of Business Computing
Bochra Rabbouch: École Pluridisciplinaire Internationale de Sousse, Department of Business Computing
Foued Saâdaoui: King Abdulaziz University, Department of Statistics

A chapter in Data Analytics for Management, Banking and Finance, 2023, pp 113-134 from Springer

Abstract: 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.

Keywords: Multiresolution data mining; Wavelets; Empirical mode decomposition; Finance; MATLAB (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-36570-6_5

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DOI: 10.1007/978-3-031-36570-6_5

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