Title: Benchmark Datasets for Lead-Lag Forecasting on Social Platforms
Primary area: datasets and benchmarks
Keywords: Time Series Data, Long-term Forecasting, Datasets and Benchmarks, Social Platform Dynamics

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 (downloads → citations of 500K papers) and GitHub (pushes → stars/forks of 1.4M 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). We documented all technical details of data curation and cleaning, verified the presence of lead-lag dynamics through statistical and classification tests, and benchmarked deep learning architectures alongside 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.