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VERSION:2.0
PRODID:-//Memento EPFL//
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SUMMARY:Emphasising extreme events when evaluating probabilistic forecasts
DTSTART:20260417T151500
DTEND:20260417T161500
DTSTAMP:20260502T080002Z
UID:95f92f235b42b09a075aac71c71ef5b1f09941a3ac42f982b1387f3e
CATEGORIES:Conferences - Seminars
DESCRIPTION:Sam Allen\, KIT\nIt is becoming increasingly common to issue f
 orecasts that are probabilistic\, in the form of probability distributions
  over all possible outcomes. To generate good probabilistic forecasts\, we
  must first be able to evaluate how good a forecast is. The evaluation of 
 probabilistic forecasts focuses on two aspects of forecast performance: fo
 recast accuracy and forecast calibration. Forecast accuracy refers to how 
 'close' the forecast is to the corresponding observation\, which can be qu
 antified using proper scoring rules. Forecast calibration considers whethe
 r probabilistic forecasts are trustworthy. While most scoring rules and ca
 libration checks treat all possible outcomes equally\, some outcomes are o
 ften of more interest than others when evaluating forecasts\, and one coul
 d argue that these outcomes should therefore be emphasised during forecast
  evaluation. For example\, extreme outcomes typically lead to the largest
  impacts on forecast users\, making accurate and calibrated forecasts for 
 these outcomes particularly valuable. In this talk\, we discuss methods to
  focus on particular outcomes when evaluating probabilistic forecasts. We 
 review weighted scoring rules\, which allow practitioners to incorporate a
  weight function into conventional scoring rules when calculating forecast
  accuracy\, and we demonstrate that the theory underlying weighted scoring
  rules can readily be extended to forecast calibration. Using this\, we in
 troduce methods to evaluate the calibration of probabilistic forecasts for
  extreme events.\n 
LOCATION:CM 1 517 https://plan.epfl.ch/?room==CM%201%20517
STATUS:CONFIRMED
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