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).
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.
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.
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 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.
Interrupted time-series analysis of weekly cereal sales using segmented regression, ARMA(1,1) residual modeling, diagnostics, and rolling forecasts.
Loss cost trend analysis for insurance pricing — frequency/severity decomposition, ONS index integration, structural break detection (154 tests)
Credit risk forecasting using unemployment, cointegration, ECM, structural breaks and out-of-time model validation. Chilean banking data.
Daily Svensson estimation of the French OAT zero-coupon curve, 1987-2026: political risk, PCA by regime and regime detection. MSc Finance thesis, University of Pavia
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.
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.
Replication Files and Notes for: Level Breaks and Finite-Sample GLS Detrending: The Point-Optimal Unit Root Test and the Purchasing Power Parity Puzzle
R time series analysis of Google Books Ngram usage of "peace" using breakpoints, Markov switching, and ARIMA.
Real-time structural-break detection in time series: whitening by the series' own history, CUSUM/GLR/Shiryaev-Roberts on the whitened stream, LightGBM rankers and causal TCNs. ~Rank 160 of 1,716 in the ADIA Lab / CrunchDAO challenge, with the full 158-experiment research record run with an LLM assistant.
Quantify SHAP feature importance stability across structural breaks in time-series data
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