Examples

Two worked notebooks run the whole method on genuinely different public datasets, so the approach is shown beyond any single domain. Both are in the examples/ directory, and each bundles a small public data snapshot so it runs offline and reproduces the same figures.

Finance: market volatility regimes

01_finance_market_regimes.ipynb

A model for next-day change in the VIX (the equity volatility index), built from public FRED series. The regimes are found from the data with detect_breaks. The result is instability: the model’s drivers reorganise in the 2008 to 2009 crisis, which lands in the unstable band. This shows the automatic regime-detection path and a case where the drivers shift.

Energy: electricity demand eras

02_energy_demand_regimes.ipynb

A day-ahead model for Great Britain electricity demand, built from public NESO data. The regimes are hand-labelled as contiguous periods of UK demand history (the higher-demand early 2010s, the efficiency-driven decline, the 2020 lockdown, and the post-lockdown plateau). The result is stability: the drivers hold across every era, and the short lockdown era is read with bootstrap confidence intervals. This shows the hand-labelled regime path and a case where the drivers hold.

Running them

Install the examples extra and open the notebooks:

pip install -e ".[examples]"
jupyter notebook