About
This skill provides a comprehensive framework for identifying regime changes in financial markets using Gaussian Process Change-Point Detection (GP-CPD). It enables developers and data scientists to segment time-series data into stationary periods, detect momentum crashes, and construct high-quality context sets for few-shot learning models. By utilizing Matérn 3/2 kernels and likelihood ratio tests, it offers a mathematically robust approach to distinguishing between different market states—such as volatility spikes or trend reversals—ultimately improving the accuracy of trading strategies and predictive financial models.
