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.
An interactive web-based dashboard to track covid-19 in real time.The Lancet infectious diseases, 20(5):533–534
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SpatialEpiBench shows adjacency-informed models with epidemic priors underperform a last-value baseline across 11 datasets from 1 day to 1 month ahead, identifying failures in outbreak anticipation, sparsity handling, and geographic adjacency utility.
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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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SpatialEpiBench: Benchmarking Spatial Information and Epidemic Priors in Forecasting
SpatialEpiBench shows adjacency-informed models with epidemic priors underperform a last-value baseline across 11 datasets from 1 day to 1 month ahead, identifying failures in outbreak anticipation, sparsity handling, and geographic adjacency utility.