Enhance Python Anomaly Detection Algorithm

I have an existing Python algorithm used for large-scale time-series signal analysis and anomaly detection. The code is functional and runs without syntax errors, but there is a subtle issue in the algorithm that causes incorrect results for certain input patterns.

The algorithm performs several processing stages, including signal normalization, sliding-window feature extraction, correlation analysis, dimensionality reduction, clustering, and anomaly scoring. It uses NumPy/SciPy and is designed to process relatively large datasets efficiently.

The main task is to:

- Review and understand the existing Python algorithm
- Identify the underlying algorithmic/logical issue
- Fix the problem without unnecessarily rewriting the entire system
- Verify that the corrected algorithm produces consistent and accurate results
- Test the solution against edge cases and larger datasets
- Check for any performance issues introduced by the fix
- Provide a short explanation of what was wrong and why the correction works

Important: The problem is not a simple syntax error. The current implementation executes successfully, but under particular data distributions the algorithm generates incorrect anomaly scores and occasionally assigns observations to the wrong cluster.

The successful freelancer should be comfortable with advanced Python, NumPy/SciPy, numerical algorithms, statistics, vectorized operations, time-series processing, and debugging complex mathematical logic.

I will provide the existing Python source code and test data to the selected freelancer.

Please include examples of similar algorithm debugging, numerical Python, or scientific-computing work you have completed.