Wavelets as Features for Time Series ML

In this project, the idea would be to familiarize oneself further with the following concepts

  • time series data
  • Wavelet analysis to generate features
  • several types of machine learning models
    • kernel ridge regression
    • multilayer perceptron
    • radial basis function networks
    • transfer learning using some well-known image classifier

Application data can range from Quantum Chemistry over Finance to Health, hence is very broad.
The main objective would be to start with a “black box” approach, i.e. using some existing implementation of a continuous wavelet filter bank and then to develop a deeper understanding on how the choice of some parameters in the wavelet filter bank influences the prediction quality.

A first reference: