← Back to all news
arXiv Machine Learning By Parvin Malekzadeh, Opher Baron, Dmitry Krass

A Unified Particle Filter LSTM for Data-Driven Process Simulation

Read the original on arXiv Machine Learning →

The paper introduces a Unified Particle Filter LSTM (Unified PF‑LSTM) for data‑driven process simulation, which maintains a weighted set of recurrent‑state hypotheses to better capture latent process conditions from incomplete event logs. By summarizing this particle belief with a weighted mean and moment‑generating‑function features, the model predicts next‑activity probabilities and conditional sojourn‑time quantiles. Experiments on three real‑world emergency department datasets show that the framework consistently outperforms existing data‑driven baselines in reproducing routing, duration, and system‑level behavior, especially when process dynamics are only partially reflected in the logs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
3d ago

DES-JEPA: Learning a Shared Vocabulary for High-Dimensional Discrete Event Sequences

DES-JEPA introduces a joint-embedding predictive architecture that learns a shared discrete vocabulary from synthetic stochastic processes with unaligned event vocabularies. Coupled with DES-PFN, a prior‑fitted network that reuses this vocabulary for in‑context next‑event prediction, the framework can handle up to 700 distinct event types without task‑specific fine‑tuning. It achieves competitive zero‑shot performance on eight unseen real‑world datasets, addressing the limitations of current foundation models for high‑dimensional discrete event sequences.

By Hugo Math, Maximilian Forstenh\"ausler, Christos Anagnostopoulos
ragfine-tuning
More like this →
Hugging Face Trending Papers
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.

rag
More like this →
arXiv Machine Learning
Jul 31

Revisiting Predictive Process Monitoring in the Age of Foundation Models: A Comparative Study of Sequence, Tabular, and LLM Approaches

arXiv:2607. 27797v1 Announce Type: new Abstract: Predictive process monitoring (PPM) leverages event logs to forecast the future of running process instances, for instance, predicting the next activity, the remaining time until case completion, or the time to the next event.

By Lennart Fertig, Lukas Kirchdorfer, Tobias Sesterhenn
llmsbenchmarks
More like this →
arXiv Machine Learning
Aug 12

ChronoSSM: Training for Temporally Aware Representations in Autoregressive State Space Models

arXiv:2608. 10120v1 Announce Type: new Abstract: Modern sequence models, from Transformers to State Space Models, have enabled powerful generative modeling across diverse domains, yet they are typically trained to predict what happens while treating when it happens as a secondary concern.

By Adrien Schoen, Nachiketa Ratnakar Patil, Arjun Bhagoji, Francesco Bronzino
llms
More like this →