10 uncertainty quantification methods in nnetsauce forecasting

This week, I released (Python) version 0.22.4 of nnetsauce. nnetsauce now contains 10 uncertainty quantification methods for time series forecasting:

• gaussian: simple, fast, but: assumes stationarity of Gaussian in-sample residuals and independence in the multivariate case
• kde: based on Kernel Density Estimation of in-sample residuals
• bootstrap: based on independent bootstrap of in-sample residuals
• block-bootstrap: based on moving block bootstrap of in-sample residuals
• scp-kde: Split conformal prediction with Kernel Density Estimation of calibrated residuals
• scp-bootstrap: Split conformal prediction with independent bootstrap of calibrated residuals
• scp-block-bootstrap: Split conformal prediction with moving block bootstrap of calibrated residuals
• scp2-kde: Split conformal prediction with Kernel Density Estimation of standardized calibrated residuals
• scp2-bootstrap: Split conformal prediction with independent bootstrap of standardized calibrated residuals
• scp2-block-bootstrap: Split conformal prediction with moving block bootstrap of standardized calibrated residuals

The release is available on Github, Conda and PyPI.

I’ll present nnetsauce and these methods with more details and examples at the 44th International Symposium on Forecasting (ISF) (ISF) 2024 (on Wednesday). I hope to see you there, with your questions, remarks and suggestions.

Next week, I’ll release a stable (and documented) version of learningmachine.