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Examples - #2
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Three notebooks in notebooks/: - figure_simulated.ipynb: Figures 1 & 2 (simulated piecewise trend + 2 seasonalities + outliers) - figure_airtraffic.ipynb: airline traffic comparison + decomposition figures - figure_electricity.ipynb: Figures 3-5 (NY electricity demand, Ks=[7,365], obsSV='SV') Each notebook: - runs fit_BASTION with paper-quality MCMC settings - saves output PDFs to outputs/ - loads R reference .rds files for comparison panels with graceful fallback - uses the pybastion conda kernel
- Rename figure_* -> example_* (simulated, airtraffic, electricity) - Add BASTION methodology introduction to each notebook - Remove TBATS/MSTL/STR comparison code (not in pybastion) - Add paper reference (arXiv:2601.18052) to each notebook - Add Tables 2 & 3 metrics markdown cells as reference - Simulated: 14 cells with true-vs-estimated component comparison - Airtraffic: 11 cells with COVID-19 structural break annotation - Electricity: 11 cells with SV LaTeX formulation and volatility plot
- Add QUICK_MODE toggle (True = fast demo, False = publication quality) - Fix electricity column names: 'Data.Date' and 'Demand..MW.' - Fix volatility key: summary['Volatility'] (was 'Volat_sum') - Simulated: replace markdown metric tables with code cells that compute MSE (Table 2) and pointwise coverage (Table 3), adding 'this run' column alongside paper reference values - Airtraffic/Electricity: add code cell computing in-sample fit statistics (R2, RMSE, component ranges, variance shares) displayed as a table - All notebooks execute successfully in QUICK_MODE
…eeping any element after burn-in
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Added and fixed examples notebooks to replicate the original paper.
Some bug fixes added to the python translation