[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290096-105":59,"doc-detail-290096-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","evaluating-probabilistic-forecasts-with-scoringrules","Evaluating Probabilistic Forecasts with scoringRules","","Probabilistic forecasts represented as predictive probability distributions are increasingly used across meteorology, hydrology, economics, and demography. Multiple statistical models and data sources can produce such forecasts, making principled model comparison essential. The scoringRules package for R enables comparative evaluation using proper scoring rules across many applied scenarios. The paper details implementation and usage, presents case studies in meteorology and economics, and references supporting background literature.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/evaluating-probabilistic-forecasts-with-scoringrules/290096/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/evaluating-probabilistic-forecasts-with-scoringrules/290096.png","ImageObject",300,407,{"name":92,"@type":93},"Kyle","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why are scoring rules important for probabilistic forecast evaluation?","Question",{"text":113,"@type":114},"Scoring rules provide functions S(F, y) that measure how accurate a forecast distribution F is when an outcome y is observed, enabling principled comparison across competing models.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What does the scoringRules R package provide?",{"text":118,"@type":114},"It provides implementations for computing proper scoring rules for many distribution types used in applied work, including CRPS, logarithmic score, multivariate energy score, and variogram score.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the paper position comparative versus absolute forecast evaluation?",{"text":122,"@type":114},"Comparative evaluation aims to determine whether model A or B is better under a proper scoring rule, while absolute evaluation focuses on diagnostics such as whether predictive distributions match observations statistically.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290096,1789640455,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},3985741905716,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","Evaluating Probabilistic Forecasts with scoringRules  \nAlexander Jordan  \nHeidelberg Institute for Theoretical Studies  \nFabian Krüger  \nKarlsruhe Institute of Technology  \nSebastian Lerch  \nKarlsruhe Institute of Technology  \nHeidelberg Institute for Theoretical Studies  \nAbstract  \nProbabilistic forecasts in the form of probability distributions over future events have become popular in several 􀀜elds including meteorology, hydrology, economics, and demography. In typical applications, many alternative statistical models and data sources can be used to produce probabilistic forecasts. Hence, evaluating and selecting among competing methods is an important task. The scoringRules package for R provides functionality for comparative evaluation of probabilistic models based on proper scoring rules, covering a wide range of situations in applied work. This paper discusses implementation and usage details, presents case studies from meteorology and economics, and points to the relevant background literature.  \nKeywords: comparative evaluation, ensemble forecasts, out-of-sample evaluation, predictive distributions, proper scoring rules, score computation, R.  \nPreface  \nThis vignette corresponds to an article of the same name in the Journal of Statistical Software (Jordan et al. 2019) . The two articles are close to identical at the time of this writing (May 23, 2023) . Starting with version 1.1.1 (released on CRAN in May 2023), scoringRules also covers threshold and outcome weighted scoring rules. The latter are described in a further vignette called ’Weighted scoringRules’ (Allen 2023) .  \n1 . Introduction: Forecast evaluation  \nForecasts are generally surrounded by uncertainty, and being able to quantify this uncertainty is key to good decision making. Accordingly, probabilistic forecasts in the form of predictive probability distributions over future quantities or events have become popular over the last decades in various 􀀜elds including meteorology, climate science, hydrology, seismology, economics, 􀀜nance, demography and political science. Important examples include the United Nation’s probabilistic population forecasts (Raftery et al. 2014), in􀀝ation projections issued by the Bank of England (e.g. , Clements 2004), or the now widespread use of probabilistic ensemble methods in meteorology (Gneiting and Raftery 2005; Leutbecher and Palmer 2008) . For recent reviews see Gneiting and Katzfuss (2014) and Raftery (2016) .  \n2 Evaluating Probabilistic Forecasts  \nWith the proliferation of probabilistic models arises the need for tools to evaluate the appropriateness of models and forecasts in a principled way. Various measures of forecast performance have been developed over the past decades to address this demand. Scoring rules are functions S (F, y) that evaluate the accuracy of a forecast distribution F, given that an outcome y was observed. As such, they allow to compare alternative models, a crucial ability given the variety of theories, data sources and statistical speci􀀜cations available in many situations. Conceptually, scoring rules can be thought of as error measures for distribution functions: While the squared error SE (x, y) = (y − x)2 measures the performance of a point forecast x, a scoring rule S (F, y) measures the performance of a distribution forecast F.  \nThis paper introduces the R (R Core Team 2017) software package scoringRules (Jordan et al. 2022), which provides functions to compute scoring rules for a variety of distributions F that come up in applied work, and popular choices of S. Two main classes of probabilistic forecasts are parametric distributions and distributions that are not known analytically, but are indirectly described through a sample of simulation draws. For example, Bayesian forecasts produced via Markov chain Monte Carlo (MCMC) methods take the latter form. Hence, the scoringRules package provides a general framework for model evaluation that covers both classical (frequentist) and Bayes","cbCaino3eehwRZvI","https://ap.wps.com/l/cbCaino3eehwRZvI","pdf",357069,38,"English","# Abstract\n# Preface\n# 1. Introduction: Forecast evaluation\n# 2. Evaluating Probabilistic Forecasts","[{\"question\":\"Why are scoring rules important for probabilistic forecast evaluation?\",\"answer\":\"Scoring rules provide functions S(F, y) that measure how accurate a forecast distribution F is when an outcome y is observed, enabling principled comparison across competing models.\"},{\"question\":\"What does the scoringRules R package provide?\",\"answer\":\"It provides implementations for computing proper scoring rules for many distribution types used in applied work, including CRPS, logarithmic score, multivariate energy score, and variogram score.\"},{\"question\":\"How does the paper position comparative versus absolute forecast evaluation?\",\"answer\":\"Comparative evaluation aims to determine whether model A or B is better under a proper scoring rule, while absolute evaluation focuses on diagnostics such as whether predictive distributions match observations statistically.\"}]","Evaluating Probabilistic Forecasts with scoringRules | PDF",96]