How to model Human Actions distribution with Event Sequence Data
It addresses forecasting problems in domains like retail and healthcare, offering practical guidance for more accurate systems, but is incremental in its methodological contributions.
This paper tackles forecasting the future distribution of events in human action sequences by challenging the autoregressive paradigm and showing that an explicit distribution forecasting objective outperforms implicit baselines, with findings that mode collapse is driven by distributional imbalance.
This paper studies forecasting of the future distribution of events in human action sequences, a task essential in domains like retail, finance, healthcare, and recommendation systems where the precise temporal order is often less critical than the set of outcomes. We challenge the dominant autoregressive paradigm and investigate whether explicitly modeling the future distribution or order-invariant multi-token approaches outperform order-preserving methods. We analyze local order invariance and introduce a KL-based metric to quantify temporal drift. We find that a simple explicit distribution forecasting objective consistently surpasses complex implicit baselines. We further demonstrate that mode collapse of predicted categories is primarily driven by distributional imbalance. This work provides a principled framework for selecting modeling strategies and offers practical guidance for building more accurate and robust forecasting systems.