Zobrazeno 1 - 10
of 2 210
pro vyhledávání: '"prediction regions"'
Autor:
English, Eshant, Lippert, Christoph
Conformal Prediction offers a powerful framework for quantifying uncertainty in machine learning models, enabling the construction of prediction sets with finite-sample validity guarantees. While easily adaptable to non-probabilistic models, applying
Externí odkaz:
http://arxiv.org/abs/2411.17042
Autor:
OSTRIHOŇ, Filip1 filip.ostrihon@savba.sk, FIŠERA, Boris2
Publikováno v:
Finance a Uver: Czech Journal of Economics & Finance. 2024, Vol. 74 Issue 4, p432-472. 41p.
Autor:
English, Eshant
Machine Learning algorithms are notorious for providing point predictions but not prediction intervals. There are many applications where one requires confidence in predictions and prediction intervals. Stringing together, these intervals give rise t
Externí odkaz:
http://arxiv.org/abs/2405.12234
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
International Journal of Electrical Power & Energy Systems, Vol 162, Iss , Pp 110244- (2024)
The aim of this paper is to compute one-day-ahead prediction regions for daily curves of electricity demand and price. Three model-based procedures to construct general prediction regions are proposed, all of them using bootstrap algorithms. The firs
Externí odkaz:
https://doaj.org/article/056d3fc2fabd41c08f9974ed824240e1
For exponentially distributed lifetimes, we consider the prediction of future order statistics based on having observed the first $m$ order statistics. We focus on the previously less explored aspects of predicting: (i) an arbitrary pair of future or
Externí odkaz:
http://arxiv.org/abs/2403.06718
Sequences of labeled events observed at irregular intervals in continuous time are ubiquitous across various fields. Temporal Point Processes (TPPs) provide a mathematical framework for modeling these sequences, enabling inferences such as predicting
Externí odkaz:
http://arxiv.org/abs/2401.04612
Multi-Modal Conformal Prediction Regions with Simple Structures by Optimizing Convex Shape Templates
Autor:
Tumu, Renukanandan, Cleaveland, Matthew, Mangharam, Rahul, Pappas, George J., Lindemann, Lars
Conformal prediction is a statistical tool for producing prediction regions for machine learning models that are valid with high probability. A key component of conformal prediction algorithms is a \emph{non-conformity score function} that quantifies
Externí odkaz:
http://arxiv.org/abs/2312.07434
Autor:
Alizadeh, M.1 (AUTHOR), Asgharzadeh, A.1 (AUTHOR) a.asgharzadeh@umz.ac.ir, Basiri, E.2 (AUTHOR)
Publikováno v:
Quality Technology & Quantitative Management. May2024, p1-24. 24p. 4 Illustrations, 3 Charts.
Conformal prediction is a statistical tool for producing prediction regions of machine learning models that are valid with high probability. However, applying conformal prediction to time series data leads to conservative prediction regions. In fact,
Externí odkaz:
http://arxiv.org/abs/2304.01075