{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T22:24:22Z","timestamp":1742941462790,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031434143"},{"type":"electronic","value":"9783031434150"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43415-0_12","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T08:01:51Z","timestamp":1694851311000},"page":"190-205","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Scoring Rule Nets: Beyond Mean Target Prediction in\u00a0Multivariate Regression"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5190-5596","authenticated-orcid":false,"given":"Daan","family":"Roordink","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2557-4604","authenticated-orcid":false,"given":"Sibylle","family":"Hess","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"key":"12_CR1","unstructured":"Aggarwal, K., Kirchmeyer, M., Yadav, P., Keerthi, S.S., Gallinari, P.: Regression with conditional gan (2019). http:\/\/arxiv.org\/abs\/1905.12868"},{"key":"12_CR2","doi-asserted-by":"publisher","unstructured":"Alexander, C., Coulon, M., Han, Y., Meng, X.: Evaluating the discrimination ability of proper multi-variate scoring rules. Ann. Oper. Res. (C) (2022). https:\/\/doi.org\/10.1016\/j.apenergy.2011.1. https:\/\/ideas.repec.org\/a\/eee\/appene\/v96y2012icp12-20.html","DOI":"10.1016\/j.apenergy.2011.1"},{"key":"12_CR3","unstructured":"Avati, A., Duan, T., Zhou, S., Jung, K., Shah, N.H., Ng, A.Y.: Countdown regression: sharp and calibrated survival predictions. In: Adams, R.P., Gogate, V. (eds.) Proceedings of The 35th Uncertainty in Artificial Intelligence Conference. Proceedings of Machine Learning Research, vol. 115, pp. 145\u2013155. PMLR (2020). https:\/\/proceedings.mlr.press\/v115\/avati20a.html"},{"key":"12_CR4","doi-asserted-by":"publisher","unstructured":"Bjerreg\u00e5rd, M.B., M\u00f8ller, J.K., Madsen, H.: An introduction to multivariate probabilistic forecast evaluation. Energy AI 4, 100058 (2021). https:\/\/doi.org\/10.1016\/j.egyai.2021.100058. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2666546821000124","DOI":"10.1016\/j.egyai.2021.100058"},{"key":"12_CR5","unstructured":"Canadian Meteorological Centre: Gem, the global environmental multiscale model (2020). https:\/\/collaboration.cmc.ec.gc.ca\/science\/rpn\/gef_html_public\/index.html. Accessed 03 May 2023"},{"key":"12_CR6","unstructured":"Canadian Meteorological Centre: Geps, the global ensemble prediction system (2021). https:\/\/weather.gc.ca\/grib\/grib2_ens_geps_e.html. Accessed 13 May 2023"},{"key":"12_CR7","doi-asserted-by":"publisher","unstructured":"Carney, M., Cunningham, P., Dowling, J., Lee, C.: Predicting probability distributions for surf height using an ensemble of mixture density networks. In: Proceedings of the 22nd International Conference on Machine Learning - ICML 2005. ACM Press (2005). https:\/\/doi.org\/10.1145\/1102351.1102366","DOI":"10.1145\/1102351.1102366"},{"key":"12_CR8","unstructured":"DWD Climate Data Center (CDC): Historical hourly station observations of solar incoming (total\/diffuse) and longwave downward radiation for germany (1981\u20132021)"},{"key":"12_CR9","doi-asserted-by":"publisher","unstructured":"Gebetsberger, M., Messner, J., Mayr, G., Zeileis, A.: Estimation methods for nonhomogeneous regression models: minimum continuous ranked probability score versus maximum likelihood. Monthly Weather Rev. 146 (2018). https:\/\/doi.org\/10.1175\/MWR-D-17-0364.1","DOI":"10.1175\/MWR-D-17-0364.1"},{"key":"12_CR10","doi-asserted-by":"publisher","unstructured":"Gneiting, T., Balabdaoui, F., Raftery, A.E.: Probabilistic forecasts, calibration and sharpness. J. Royal Stat. Soc. Series B (Stat. Methodol.) 69(2), 243\u2013268 (2007). https:\/\/doi.org\/10.1111\/j.1467-9868.2007.00587.x. https:\/\/rss.onlinelibrary.wiley.com\/doi\/abs\/10.1111\/j.1467-9868.2007.00587.x","DOI":"10.1111\/j.1467-9868.2007.00587.x"},{"issue":"1","key":"12_CR11","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1146\/annurev-statistics-062713-085831","volume":"1","author":"T Gneiting","year":"2014","unstructured":"Gneiting, T., Katzfuss, M.: Probabilistic forecasting. Ann. Rev. Stat. Appl. 1(1), 125\u2013151 (2014). https:\/\/doi.org\/10.1146\/annurev-statistics-062713-085831","journal-title":"Ann. Rev. Stat. Appl."},{"issue":"477","key":"12_CR12","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1198\/016214506000001437","volume":"102","author":"T Gneiting","year":"2007","unstructured":"Gneiting, T., Raftery, A.E.: Strictly proper scoring rules, prediction, and estimation. J. Am. Stat. Assoc. 102(477), 359\u2013378 (2007). https:\/\/doi.org\/10.1198\/016214506000001437","journal-title":"J. Am. Stat. Assoc."},{"key":"12_CR13","doi-asserted-by":"publisher","unstructured":"Grimit, E.P., Gneiting, T., Berrocal, V.J., Johnson, N.A.: The continuous ranked probability score for circular variables and its application to mesoscale forecast ensemble verification. Q. J. Royal Meteorol. Soc. 132(621C), 2925\u20132942 (2006). https:\/\/doi.org\/10.1256\/qj.05.235. https:\/\/rmets.onlinelibrary.wiley.com\/doi\/abs\/10.1256\/qj.05.235","DOI":"10.1256\/qj.05.235"},{"key":"12_CR14","doi-asserted-by":"publisher","DOI":"10.4324\/9780203451519","volume-title":"An Introduction to Neural Networks","author":"K Gurney","year":"1997","unstructured":"Gurney, K.: An Introduction to Neural Networks. Taylor & Francis Inc., Boston (1997)"},{"key":"12_CR15","doi-asserted-by":"publisher","unstructured":"Haynes, W.: Encyclopedia of Systems Biology, pp. 1190\u20131191. Springer, New York (2013). https:\/\/doi.org\/10.1007\/978-1-4419-9863-7_1235","DOI":"10.1007\/978-1-4419-9863-7_1235"},{"issue":"12","key":"12_CR16","doi-asserted-by":"publisher","first-page":"1885","DOI":"10.1093\/jamia\/ocaa140","volume":"27","author":"Y Jiao","year":"2020","unstructured":"Jiao, Y., Sharma, A., Ben Abdallah, A., Maddox, T.M., Kannampallil, T.: Probabilistic forecasting of surgical case duration using machine learning: model development and validation. J. Am. Med. Inf. Assoc. 27(12), 1885\u20131893 (2020)","journal-title":"J. Am. Med. Inf. Assoc."},{"key":"12_CR17","doi-asserted-by":"publisher","unstructured":"Jordan, A., Kr\u00fcger, F., Lerch, S.: Evaluating probabilistic forecasts with scoringrules. J. Stat. Softw. 90(12), 1\u201337 (2019). https:\/\/doi.org\/10.18637\/jss.v090.i12, https:\/\/www.jstatsoft.org\/index.php\/jss\/article\/view\/v090i12","DOI":"10.18637\/jss.v090.i12"},{"key":"12_CR18","doi-asserted-by":"publisher","unstructured":"Kanazawa, T., Gupta, C.: Sample-based uncertainty quantification with a single deterministic neural network (2022). https:\/\/doi.org\/10.48550\/ARXIV.2209.08418","DOI":"10.48550\/ARXIV.2209.08418"},{"key":"12_CR19","unstructured":"Koninklijk Nederlands Meteorologisch Instituut: Uurgegevens van het weer in nederland (2008\u20132020). http:\/\/projects.knmi.nl\/klimatologie\/uurgegevens\/. Accessed 03 May 2023"},{"key":"12_CR20","doi-asserted-by":"crossref","unstructured":"Matheson, J.E., Winkler, R.L.: Scoring rules for continuous probability distributions. Manag. Sci. 22(10), 1087\u20131096 (1976). http:\/\/www.jstor.org\/stable\/2629907","DOI":"10.1287\/mnsc.22.10.1087"},{"key":"12_CR21","doi-asserted-by":"crossref","unstructured":"Murad, A., Kraemer, F.A., Bach, K., Taylor, G.: Probabilistic deep learning to quantify uncertainty in air quality forecasting. Sensors (Basel) 21(23) (2021)","DOI":"10.3390\/s21238009"},{"key":"12_CR22","doi-asserted-by":"publisher","unstructured":"Muschinski, T., Mayr, G.J., Simon, T., Umlauf, N., Zeileis, A.: Cholesky-based multivariate gaussian regression. Econometrics Stat. (2022). https:\/\/doi.org\/10.1016\/j.ecosta.2022.03.001","DOI":"10.1016\/j.ecosta.2022.03.001"},{"key":"12_CR23","unstructured":"National Centers for Environmental Information: Global forecast system (gfs)l(2020). https:\/\/www.ncei.noaa.gov\/products\/weather-climate-models. Accessed 03 May 2023"},{"issue":"3","key":"12_CR24","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1007\/s00180-014-0523-0","volume":"30","author":"J Nowotarski","year":"2014","unstructured":"Nowotarski, J., Weron, R.: Computing electricity spot price prediction intervals using quantile regression and forecast averaging. Comput. Stat. 30(3), 791\u2013803 (2014). https:\/\/doi.org\/10.1007\/s00180-014-0523-0","journal-title":"Comput. Stat."},{"key":"12_CR25","unstructured":"Pinson, P., Tastu, J.: Discrimination ability of the Energy score. No. 15 in DTU Compute-Technical Report-2013, Technical University of Denmark (2013)"},{"issue":"11","key":"12_CR26","doi-asserted-by":"publisher","first-page":"3885","DOI":"10.1175\/MWR-D-18-0187.1","volume":"146","author":"S Rasp","year":"2018","unstructured":"Rasp, S., Lerch, S.: Neural networks for postprocessing ensemble weather forecasts. Monthly Weather Rev. 146(11), 3885\u20133900 (2018). https:\/\/doi.org\/10.1175\/MWR-D-18-0187.1","journal-title":"Monthly Weather Rev."},{"key":"12_CR27","doi-asserted-by":"publisher","first-page":"1321","DOI":"10.1175\/MWR-D-14-00269.1","volume":"143","author":"M Scheuerer","year":"2015","unstructured":"Scheuerer, M., Hamill, T.: Variogram-based proper scoring rules for probabilistic forecasts of multivariate quantities*. Monthly Weather Rev. 143, 1321\u20131334 (2015). https:\/\/doi.org\/10.1175\/MWR-D-14-00269.1","journal-title":"Monthly Weather Rev."},{"key":"12_CR28","unstructured":"Viroli, C., McLachlan, G.J.: Deep gaussian mixture models (2017). https:\/\/arxiv.org\/abs\/1711.06929, ArXiv-preprint:1711.06929"},{"key":"12_CR29","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Toth, Z., Wobus, R., Richardson, D., Mylne, K.: The economic value of ensemble-based weather forecasts. Bull. Am. Meteorol. Soc. 83(1), 73\u201383 (2002). http:\/\/www.jstor.org\/stable\/26215325","DOI":"10.1175\/1520-0477(2002)083<0073:TEVOEB>2.3.CO;2"},{"key":"12_CR30","doi-asserted-by":"publisher","unstructured":"\u00d6nkal, D., Murado\u01e7lu, G.: Evaluating probabilistic forecasts of stock prices in a developing stock market. Eur. J. Oper. Res. 74(2), 350\u2013358 (1994). https:\/\/doi.org\/10.1016\/0377-2217(94)90102-3. https:\/\/www.sciencedirect.com\/science\/article\/pii\/0377221794901023, financial Modelling","DOI":"10.1016\/0377-2217(94)90102-3"},{"key":"12_CR31","unstructured":"\u0106evid, D., Michel, L., N\u00e4f, J., Meinshausen, N., B\u00fchlmann, P.: Distributional random forests: heterogeneity adjustment and multivariate distributional regression (2020)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43415-0_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T08:04:43Z","timestamp":1694851483000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43415-0_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434143","9783031434150"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43415-0_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"829","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"196","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.63","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}