TailedTS supplies 24.69 billion Wikipedia page-view records as a public benchmark for heavy-tailed time series forecasting and periodicity analysis, revealing weaker periodic structure in high-traffic pages.
John Wiley & Sons
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
AgriPriceBD dataset of 1779 daily prices released; naive persistence outperforms deep models like Informer and Time2Vec-Transformer on heterogeneous Bangladeshi commodity series with statistical validation.
StockR1 unifies LLM-based financial reasoning and time-series forecasting by emitting verifiable forecast actions that condition a decoder, optimized via consistency-grounded RL to improve accuracy on QA and prediction tasks.
Vertex misalignment impairs changepoint localization in network time series when the signal is in joint distributions of latent positions, and graph matching or optimal transport cannot correct the impairment.
citing papers explorer
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TailedTS: Benchmark Dataset for Heavy-Tailed Time Series Prediction and Periodicity Quantification
TailedTS supplies 24.69 billion Wikipedia page-view records as a public benchmark for heavy-tailed time series forecasting and periodicity analysis, revealing weaker periodic structure in high-traffic pages.
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A Benchmark of Classical and Deep Learning Models for Agricultural Commodity Price Forecasting on A Novel Bangladeshi Market Price Dataset
AgriPriceBD dataset of 1779 daily prices released; naive persistence outperforms deep models like Informer and Time2Vec-Transformer on heterogeneous Bangladeshi commodity series with statistical validation.
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Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs
StockR1 unifies LLM-based financial reasoning and time-series forecasting by emitting verifiable forecast actions that condition a decoder, optimized via consistency-grounded RL to improve accuracy on QA and prediction tasks.
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Vertex misalignment and changepoint localization in network time series
Vertex misalignment impairs changepoint localization in network time series when the signal is in joint distributions of latent positions, and graph matching or optimal transport cannot correct the impairment.