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. 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
The proliferation of social platforms and the increasing maturity of time-series forecasting models are creating opportunities to formalize and address complex predictive challenges like Lead-Lag Forecasting.
This formalization could unlock significant predictive power for understanding and influencing social dynamics, market trends, and content virality, moving beyond simple correlational analysis.
The explicit definition and benchmarking of Lead-Lag Forecasting establish a new, unified problem statement for the time-series community, fostering specialized model development and data collection.
- · AI researchers and data scientists
- · Social media platforms
- · Marketing and advertising industries
- · Financial analysts
- · Organizations relying solely on lagging indicators
- · Traditional, static forecasting methods
Improved predictive accuracy for future trends based on early signals across various social and economic domains.
New tools and platforms emerge that specialize in identifying and leveraging lead-lag relationships for strategic corporate and governmental planning.
Enhanced ability to manipulate or preemptively counteract social phenomena based on early warning signs, raising ethical and regulatory concerns.
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Read at arXiv cs.LG