arXiv Machine Learning June 16, 2026 By Yahya Aalaila, Sumantrak Mukherjee, Gerrit Gro{\ss}mann, Sebastian Vollmer

HawkesNest: A Multi-Axis Synthetic Benchmark for Spatiotemporal Pattern Complexity

Read the original on arXiv Machine Learning →

arXiv:2606. 16863v1 Announce Type: new Abstract: Evaluation of spatiotemporal point process (STPP) models relies heavily on opaque real-world datasets, where latent generative structure is unknown and model failures are difficult to attribute.

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benchmarks

arXiv Machine Learning

Jul 2

Seahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modeling

arXiv:2607. 01022v1 Announce Type: new Abstract: Spatiotemporal point processes (STPPs) model event data in continuous time and space, with applications in mobility, epidemiology, and public safety.

By Yahya Aalaila, Gerrit Gro{\ss}mann, Sebastian Vollmer

benchmarks safety

arXiv Machine Learning

Jul 13

Kronecker-Structured Nonparametric Spatiotemporal Point Processes

arXiv:2603. 23746v2 Announce Type: replace Abstract: Events in spatiotemporal domains arise in numerous real-world applications, where uncovering event relationships and enabling accurate prediction are central challenges.

By Zhitong Xu, Qiwei Yuan, Yinghao Chen, Yan Sun, Bin Shen, Shandian Zhe

arXiv Machine Learning

Aug 4

A Semiparametric Discrete Hawkes Model with a Collapsed Gaussian-Process Prior

arXiv:2509. 21996v3 Announce Type: replace-cross Abstract: Hawkes processes are used in settings where past events increase the likelihood of future events occurring, resulting in a natural clustering structure.

By Trinnhallen Brisley, Gordon Ross, Daniel Paulin

arXiv Machine Learning

Jul 17

GAttNHP: Group Attention Neural Hawkes Process for Extrapolation Reasoning in Temporal Knowledge Graphs

arXiv:2607. 14733v1 Announce Type: new Abstract: Temporal Knowledge Graphs (TKGs) record how facts evolve over time, but forecasting future events on a TKG remains difficult for three reasons: (i) long-range temporal dependencies are hard to encode; (ii) events on different chains mutually excite or inhibit one another in ways that snapshot-level models cannot express; and (iii) inter-arrival times are heavy-tailed and statistically sparse, so deterministic time predictors are unreliable.

By Xiangni Tian, Kaixian Yu, Runpeng Dai, Niansheng Tang, Hongtu Zhu

benchmarks

arXiv Machine Learning

Jun 9

In-Context Learning of Temporal Point Processes with Foundation Inference Models

arXiv:2509. 24762v3 Announce Type: replace Abstract: Modeling event sequences of multiple event types with marked temporal point processes (MTPPs) provides a principled way to uncover governing dynamical rules and predict future events.

By David Berghaus, Patrick Seifner, Kostadin Cvejoski, C\'esar Ojeda, Rams\'es J. S\'anchez

llms benchmarks

arXiv AI

Jul 2

Aionoscope: Debugging Latent-State Accessibility in Time-Series Representations

arXiv:2607. 00956v1 Announce Type: cross Abstract: Time-series models are often evaluated by what they can forecast or classify, but those scores do not show whether their representations preserve the process state a user may want to inspect: event timing, phase, amplitude, frequency, or regime variables.

By Alexander Chemeris, Ming Jin, Randall Balestriero

One email a morning, machine-written

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