Benchmark Datasets for Lead-Lag Forecasting on Social Platforms

• URL: http://arxiv.org/abs/2511.03877v1
• Date: Wed, 05 Nov 2025 21:47:28 GMT
• Title: Benchmark Datasets for Lead-Lag Forecasting on Social Platforms
• 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,
• Abstract summary: Lead-Lag Forecasting: given an early usage channel (the lead), predict a correlated but temporally shifted outcome channel (the lag)<n>We present two high-volume benchmark datasets-arXiv and GitHub-and outline additional domains with analogous lead-lag dynamics.<n>Our datasets provide ideal testbeds for lead-lag forecasting, by capturing long-horizon dynamics across years, spanning the full spectrum of outcomes.
• Score: 30.166429756385767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 -> citations of 2.3M papers) and GitHub (pushes/stars -> forks of 3M repositories)-and outline additional domains with analogous lead-lag dynamics, including Wikipedia (page views -> edits), Spotify (streams -> concert attendance), e-commerce (click-throughs -> purchases), and LinkedIn profile (views -> 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/.

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