Fundamental package for quantitative finance with Python.

25 structural break detection methods for univariate time series: XGBoost, Neural Networks, Ensembles, Reinforcement Learning, and Statistical approaches. Evaluated on cross-dataset generalization.

CUSUM (Cumulative Sum) filter for detecting structural shifts in financial time series, implemented in Python

Long-run analysis (1975-present) of public and private investment in Portugal using European Commission AMECO data: robust DBnomics pipeline, structural-break and lagged-regression analysis, real (chain-linked) series, and cross-country comparison (Spain, Greece, Ireland, EU).

Loss cost trend analysis for insurance pricing — frequency/severity decomposition, ONS index integration, structural break detection (154 tests)

Bai–Perron structural break detection and estimation for time series and panel data. Tests for breaks, estimates break dates with confidence intervals, and selects break counts via sequential testing or information criteria.

Detecting regime changes in financial time series using Chow, CUSUM, and Bai-Perron. Break-aware forecasting with ARIMA, Prophet, and LSTM compared against naive baselines.

End-to-End Python implementation of Mukhia et al.'s (2025) methodology for detecting political risk transmission in stablecoin markets. Implements dynamic programming for endogenous breakpoint detection, Empirical Mode Decomposition, Cholesky-identified structural shocks, and AAFT surrogate validation to quantify political uncertainty spillovers.

Regime-switching cointegration analysis of UK wholesale gas and electricity prices, 2010–2026.

A reproducible time-series case study on regime instability and forecast failure in U.S. trade-balance forecasting.

R package for testing and estimating structural breaks in time series and panel data using Bai-Perron and dynamic programming methods. Provides hypothesis tests (supF, UDmax, WDmax), break-date estimation, and support for fixed effects and common correlated effects models.

End-to-End Python implementation of Lacava's (2026) "Shifting Correlations" research. Features Numba-compiled GJR-GARCH volatility filtering, augmented DCC-X framework with exogenous Trade Policy Uncertainty integration, structural break testing, out-of-sample GMV optimization, and Model Confidence Set validation.

End-to-End Python implementation of Liu & Cheng's (2026) methodology for U.S. Treasury yield curve forecasting. Combines Factor-Augmented Dynamic Nelson-Siegel models, High-Dimensional Random Forests, and Distributionally Robust Optimization (DRO) for risk-aware ensemble forecasting under ambiguity.

Autoregressive (AR) models with advanced techniques: model selection, diagnostics, structural breaks, rolling forecasts, Fourier seasonality, exogenous variables, business cycle analysis, and benchmarking for economic time series.

📈 Forecast U.S. Treasury yield curves with a robust machine learning approach, enhancing accuracy and decision-making in finance.