arXiv Machine LearningBy Kimia Kazemian (Department of Computer Science, Cornell University), Zhenzhen Liu (Department of Computer Science, Cornell University), Yangfanyu Yang (Department of Information Science, Cornell University), Katie Luo (Department of Computer Science, Stanford University), Shuhan Gu (Department of Computer Science, Cornell University), Audrey Du (Department of Computer Science, Cornell University), Xinyu Yang (Department of Information Science, Cornell University), Jack Jansons (Department of Computer Science, Cornell University), Kilian Q. Weinberger (Department of Computer Science, Cornell University), John Thickstun (Department of Computer Science, Cornell University), Yian Yin (Department of Information Science, Cornell University), Sarah Dean (Department of Computer Science, Cornell University)
Benchmark Datasets for Lead-Lag Forecasting on Social Platforms
arXiv:2511. 03877v2 Announce Type: replace Abstract: Social and collaborative platforms emit multivariate time-series traces in which early interactions -- such as views, likes, or downloads -- are followed, sometimes months or years later, by higher impact like citations, sales, or reviews.
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lives at arXiv Machine Learning.
arXiv:2602. 20459v2 Announce Type: replace Abstract: Can AI systems trained on the existing scientific record forecast the advances that will follow?
By Anirudh Ajith, Amanpreet Singh, Jay DeYoung, Nadav Kunievsky, Austin C. Kozlowski, Oyvind Tafjord, James Evans, Daniel S. Weld, Tom Hope, Doug Downey
arXiv:2606. 27539v1 Announce Type: cross Abstract: Social media popularity prediction aims to forecast the future reach or influence of online content from early-stage observations.
By Utkarsh Sahu, Zhisheng Qi, Li Zhu, Yizhao Yang, Jun Li, Ryan Rossi, Yu Wang
arXiv:2608. 05742v1 Announce Type: cross Abstract: Multivariate time series forecasting presents unique challenges because future variables often co-evolve under shared system dynamics.
Recent years have witnessed the emergence of multivariate modeling using time series foundation models (TSFMs), which achieve advanced zero-shot generalization. Modern multivariate TSFMs are predominantly pretrained on multivariate synthetic data, which is easier to scale but may fail to capture the complex temporal dynamics and cross-variable relationships present in real-world time series.
arXiv:2512. 23847v2 Announce Type: replace-cross Abstract: We develop a statistical procedure to detect lookahead bias in economic forecasts generated by large language models (LLMs).