25 structural break detection methods for univariate time series: XGBoost, Neural Networks, Ensembles, Reinforcement Learning, and Statistical approaches. Evaluated on cross-dataset generalization.
Fundamental package for quantitative finance with Python.
Official Python implementation of the Pioneer Detection Method (PDM) — convergence-based expert aggregation and opinion pooling under structural change. Code for Vansteenberghe (2026), The Geneva Papers 51(1).
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).
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.
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.
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.
Loss cost trend analysis for insurance pricing — frequency/severity decomposition, ONS index integration, structural break detection (154 tests)
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.
A Python-based analysis of fundamental econometric challenges, including Omitted Variable Bias (OVB), sensitivity to outliers, model selection criteria, elasticity, and time series stationarity, utilizing both simulated environments and empirical real-world financial data.
Quantify SHAP feature importance stability across structural breaks in time-series data
A reproducible time-series case study on regime instability and forecast failure in U.S. trade-balance forecasting.
Econometric analysis of UK wholesale gas and electricity prices using cointegration, structural break detection, and regime-specific modelling to investigate long-run market relationships.
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.
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.
📈 Forecast U.S. Treasury yield curves with a robust machine learning approach, enhancing accuracy and decision-making in finance.
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