DeepLévy learns mixtures of Lévy stable distributions for heavy-tailed time series forecasting by minimizing discrepancies between empirical and parametric characteristic functions, outperforming prior methods on tail risk metrics under extreme volatility.
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A new conformal framework learns polyhedral uncertainty sets tailored to robust optimization objectives, minimizing decision loss while preserving coverage via calibration and independent re-calibration.
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DeepL\'evy: Learning Heavy-Tailed Uncertainty in Highly Volatile Time Series
DeepLévy learns mixtures of Lévy stable distributions for heavy-tailed time series forecasting by minimizing discrepancies between empirical and parametric characteristic functions, outperforming prior methods on tail risk metrics under extreme volatility.
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Learning Polyhedral Conformal Sets for Robust Optimization
A new conformal framework learns polyhedral uncertainty sets tailored to robust optimization objectives, minimizing decision loss while preserving coverage via calibration and independent re-calibration.