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Investigate probabilistic forecasting #140
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Probabilistic Forecasting with Spline Quantile Function RNNs Jan Gasthaus Konstantinos Benidis Yuyang Wang Syama S. RangapuramDavid Salinas Valentin Flunkert Tim JanuschowskiAWS AI Labs http://proceedings.mlr.press/v89/gasthaus19a/gasthaus19a.pdf |
Linked with this issue : Evaluate Continuous Ranked Probability Score as a Forecasting Performance Measure #74 |
ensembleBMA: An R Package for Probabilistic Forecasting using Ensembles and Bayesian Model Averaging Chris Fraley, Adrian E. RafteryTilmann Gneiting, J. McLean Sloughter https://www.stat.washington.edu/sites/default/files/files/reports/2008/tr516.pdf |
Nice doc with sample cases/datasets https://ubc-mds.github.io/DSCI_562_regr-2/lec4/032-probabilistic_forecasting.html |
A note on the use of empirical AUC for evaluating probabilistic forecasts Simon Byrne https://projecteuclid.org/download/pdfview_1/euclid.ejs/1455715967 |
Closing |
It would be nice to have some kind of probabilistic forecasting in PyAF (version x.0)
For the moment, PyAF is predicting the future values of the signal (point forecasts). Probabilistic forecasting aims at producing forecasts for the whole distribution over the horizon instead of just producing point estimates.
Roughly speaking, Probabilistic forecasting is about producing estimates of all forecast quantiles over the horizon.
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