The DOI matches its Crossref record; 117 of the 117 references it cites were found in a registry or an index.
Probabilistic Forecasting
Abstract
A probabilistic forecast takes the form of a predictive probability distribution over future quantities or events of interest. Probabilistic forecasting aims to maximize the sharpness of the predictive distributions, subject to calibration, on the basis of the available information set. We formalize and study notions of calibration in a prediction space setting. In practice, probabilistic calibration can be checked by examining probability integral transform (PIT) histograms. Proper scoring rules such as the logarithmic score and the continuous ranked probability score serve to assess calibration and sharpness simultaneously. As a special case, consistent scoring functions provide decision-theoretically coherent tools for evaluating point forecasts. We emphasize methodological links to parametric and nonparametric distributional regression techniques, which attempt to model and to estimate conditional distribution functions; we use the context of statistically postprocessed ensemble forecasts in numerical weather prediction as an example. Throughout, we illustrate concepts and methodologies in data examples.
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Cites
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Strictly Proper Scoring Rules, Prediction, and Estimation
Gneiting, T., & Raftery, A. E. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000001437 · Cited by 5,793
Self-citationDiebold, F. X., & Mariano, R. S. (1995) · Journal of Business & Economic Statistics · doi:10.1080/07350015.1995.10524599 · Cited by 5,130
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Conditional value-at-risk for general loss distributions
Rockafellar, R. T., & Uryasev, S. (2002) · Journal of Banking & Finance · doi:10.1016/s0378-4266(02)00271-6 · Cited by 3,820
Ensemble forecasting of species distributions
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Cited by
Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond
Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A., & Hyndman, R. J. (2016) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2016.02.001 · Cited by 1,044
WeatherBench: A Benchmark Data Set for Data‐Driven Weather Forecasting
Rasp, S., Dueben, P. D., Scher, S., Weyn, J. A., Mouatadid, S., & Thuerey, N. (2020) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2020ms002203 · Cited by 663
Riebler, A., & Held, L. (2017) · Biometrical Journal · doi:10.1002/bimj.201500263 · Cited by 624
Energy Forecasting: A Review and Outlook
Hong, T., Pinson, P., Wang, Y., Weron, R., Yang, D., & Zareipour, H. (2020) · IEEE Open Access Journal of Power and Energy · doi:10.1109/oajpe.2020.3029979 · Cited by 621
Review on probabilistic forecasting of photovoltaic power production and electricity consumption
van der Meer, D. W., Widén, J., & Munkhammar, J. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.212 · Cited by 481
Metadata
- Authors
- Gneiting, T., & Katzfuss, M.
- Venue
- Annual Review of Statistics and Its Application
- Publisher
- Annual Reviews
- Published
- 2014-01-03
- Volume
- 1
- Issue
- 1
- Pages
- 125-151
- ISSN
- 2326-8298 (ISSN-L)
- 2326-831X
- OpenAlex
- W4213041519
- Cited by
- 888
- References
- 117
- FWCI
- 11.73
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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