Why use Matérn kernels instead of RBF for financial time-series?

Matérn 3/2 kernels are preferred because they are only once-differentiable and less 'infinitely smooth' than RBF kernels, making them more realistic for capturing the jagged, non-stationary dynamics of financial markets.

What is a change-point in financial data?

A change-point is a specific moment when the statistical properties of a time-series, such as its mean, variance, or correlation, shift significantly, signaling a transition to a new market regime.

What is a severity score in GP-CPD?

The severity score is a ratio of the marginal likelihood of the change-point model versus a stationary model; a score above 0.90 typically indicates strong evidence that a regime change has occurred.

How does this skill improve few-shot learning performance?

By segmenting data into clean regimes, it allows models to use more informative context sets that avoid mixing different market states, leading to an estimated 11.3% improvement in Sharpe ratios according to the X-Trend paper.

Financial Change-Point Detection

Name: Financial Change-Point Detection
Author: Donaldshen27

byDonaldshen27

데이터 과학 및 ML

Detects structural breaks and regime shifts in financial time-series using Gaussian Process models to identify market transitions.

소개

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.

주요 기능

GP-CPD algorithm for automated structural break detection
Implementation of Matérn 3/2 kernels optimized for financial dynamics
Time-series segmentation into distinct stationary regimes
Likelihood ratio-based severity scoring for transition confidence
Context set construction for regime-aware machine learning models
0 GitHub stars

사용 사례

Identifying momentum crashes and rapid trend reversals in equity markets
Segmenting historical price data for few-shot learning pattern matching
Building adaptive trading strategies that respond to volatility regime changes

소개

주요 기능

GP-CPD algorithm for automated structural break detection
Implementation of Matérn 3/2 kernels optimized for financial dynamics
Time-series segmentation into distinct stationary regimes
Likelihood ratio-based severity scoring for transition confidence
Context set construction for regime-aware machine learning models
0 GitHub stars

사용 사례

Identifying momentum crashes and rapid trend reversals in equity markets
Segmenting historical price data for few-shot learning pattern matching
Building adaptive trading strategies that respond to volatility regime changes