{"ID":2845963,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.03877","arxiv_id":"2511.03877","title":"Benchmark Datasets for Lead-Lag Forecasting on Social Platforms","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. We formalize this setting as Lead-Lag Forecasting (LLF): given an early usage channel (the lead), predict a correlated but temporally shifted outcome channel (the lag). Despite the ubiquity of such patterns, LLF has not been treated as a unified forecasting problem within the time-series community, largely due to the absence of standardized datasets. To anchor research in LLF, here we present two high-volume benchmark datasets-arXiv (accesses -\u003e citations of 2.3M papers) and GitHub (pushes/stars -\u003e forks of 3M repositories)-and outline additional domains with analogous lead-lag dynamics, including Wikipedia (page views -\u003e edits), Spotify (streams -\u003e concert attendance), e-commerce (click-throughs -\u003e purchases), and LinkedIn profile (views -\u003e messages). Our datasets provide ideal testbeds for lead-lag forecasting, by capturing long-horizon dynamics across years, spanning the full spectrum of outcomes, and avoiding survivorship bias in sampling. We documented all technical details of data curation and cleaning, verified the presence of lead-lag dynamics through statistical and classification tests, and benchmarked parametric and non-parametric baselines for regression. Our study establishes LLF as a novel forecasting paradigm and lays an empirical foundation for its systematic exploration in social and usage data. Our data portal with downloads and documentation is available at https://lead-lag-forecasting.github.io/.","short_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. We formalize this setting as Lead-Lag Forecasting (LLF): given an early usage channel...","url_abs":"https://arxiv.org/abs/2511.03877","url_pdf":"https://arxiv.org/pdf/2511.03877v1","authors":"[\"Kimia Kazemian\",\"Zhenzhen Liu\",\"Yangfanyu Yang\",\"Katie Z Luo\",\"Shuhan Gu\",\"Audrey Du\",\"Xinyu Yang\",\"Jack Jansons\",\"Kilian Q Weinberger\",\"John Thickstun\",\"Yian Yin\",\"Sarah Dean\"]","published":"2025-11-05T21:47:28Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"LoRA\"]","has_code":false}