arXiv AI By Fabian Spaeh, Sean Jaffe, Shandian Zhe, Bin Shen

Foundation Models for Event Prediction Across Heterogeneous Event Logs

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Oct 13

Foundation Models for Event Prediction Across Heterogeneous Event Logs

The paper introduces the Foundation Event Predictor (FEP), a scalable in‑context architecture that performs next‑event prediction across diverse event logs using learned, event‑level nonparametric inference. FEP employs causal self‑attention to encode context and target histories, cross‑attention for relevance‑weighted aggregation, and permutation‑robust embedding learning to decouple event IDs from semantics. Trained on 696 real‑world datasets and evaluated on 50 unseen ones, FEP generalizes effectively to new systems without parameter adaptation, often matching or surpassing models trained separately on each target dataset.

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