Multiresolution Data Analytics for Financial Time Series Using MATLAB

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.