Copulas for uncertainty quantification in time series forecasting
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On Friday (2024-07-26), I presented nnetsauce (“Probabilistic Forecasting with nnetsauce (using Density Estimation, Bayesian inference, Conformal prediction and Vine copulas)”) version 0.23.0 at an sktime (a unified interface for machine learning with time series) meetup. The news for 0.23.0 are:
- A method
cross_val_score: time series cross-validation for classesMTSandDeepMTS, with fixed and increasing window - Copula simulation (thanks to pyvinecopulib) for uncertainty quantification in classes
MTSandDeepMTS:type_pibased on copulas of in-sample residuals:vine-tll(default),vine-bb1,vine-bb6,vine-bb7,vine-bb8,vine-clayton,vine-frank,vine-gaussian,vine-gumbel,vine-indep,vine-joe,vine-studenttype_pibased on sequential split conformal prediction (scp) + vine copula based on calibrated residuals:scp-vine-tll,scp-vine-bb1,scp-vine-bb6,scp-vine-bb7,scp-vine-bb8,scp-vine-clayton,scp-vine-frank,scp-vine-gaussian,scp-vine-gumbel,scp-vine-indep,scp-vine-joe,scp-vine-studenttype_pibased on sequential split conformal prediction (scp2) + vine copula based on standardized calibrated residuals:scp2-vine-tll,scp2-vine-bb1,scp2-vine-bb6,scp2-vine-bb7,scp2-vine-bb8,scp2-vine-clayton,scp2-vine-frank,scp2-vine-gaussian,scp2-vine-gumbel,scp2-vine-indep,scp2-vine-joe,scp2-vine-student
For more details and examples of use, you can read these slides:
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