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 classes`MTS`

and`DeepMTS`

, with fixed and increasing window **Copula simulation**(thanks to pyvinecopulib) for uncertainty quantification in classes`MTS`

and`DeepMTS`

:`type_pi`

based 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-student`

`type_pi`

based 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-student`

`type_pi`

based 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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