Financial Data Prediction Based on Wavelet Analysis and Kalman Filter Algorithms

The experimental part uses real stock market data, denoising and feature extraction of the data by wavelet analysis, and Kalman filtering for state estimation and prediction. The experimental results show that the method of combining wavelet analysis and Kalman filtering can significantly improve the accuracy of prediction compared to using traditional prediction models alone.

See the github repo.