Modelling Extremal Events
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EVT-based approaches allow for more robust identification of extreme events by modeling the tails of temperature distributions, addressing the inadequacies of fixed percentile thresholds.
“EVT-based approaches allow for more robust identification of extreme events by modeling the tails of temperature distributions, addressing the inadequacies of fixed percentile thresholds.”
From Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs (2024) · cites this paper as [30]
DI-GNN incorporates GPD-derived descriptors into the feature space, adjacency matrix, and loss function to enhance its sensitivity to rare heatwave occurrences.
“DI-GNN incorporates Generalized Pareto Distribution (GPD)-derived descriptors into the feature space, adjacency matrix, and loss function to enhance its sensitivity to rare heatwave occurrences.”
From Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs (2024) · cites this paper as [30]
DI-GNN represents a significant advancement in modeling rare and extreme events, transcending the limitations of traditional feature engineering by embedding EVT principles directly into the learning process.
“The Distribution-Informed Graph Neural Network (DI-GNN) represents a significant advancement in the modeling of rare and extreme events, transcending the limitations of traditional feature engineering by embedding Extreme Value Theory (EVT) principles directly into the learning process.”
From Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs (2024) · cites this paper as [30]
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- Authors
- Embrechts, P., Klüppelberg, C., & Mikosch, T.
- Affiliations
- Technical University of Munich (DE)
- University of Copenhagen (DK)
- Venue
- Springer eBooks
- Publisher
- Springer Berlin Heidelberg
- Published
- 1997
- OpenAlex
- W2502254989
- Cited by
- 4,402
- References
- 4
- Citations by year
- 2026: 140 · 2025: 137 · 2024: 128 · 2023: 142 · 2022: 136 · 2021: 115 · 2020: 164 · 2019: 142 · 2018: 192 · 2017: 175 · 2016: 198 · 2015: 182 · 2014: 250 · 2013: 200 · 2012: 252
- FWCI
- 46.74
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Probability and Risk Models
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Risk and Volatility Modeling
- Social Sciences › Economics, Econometrics and Finance › Finance › Stochastic processes and financial applications
- Keywords
- extreme value theory, financial fluctuations, stock market shocks, risk management
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