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Transformer Hawkes Process

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arxiv 2002.09291 v5 pith:6ZJ7DM2I submitted 2020-02-21 cs.LG stat.ML

classification cs.LGstat.ML
keywords datadependenciespredictionprocesscaptureeventexistinghawkes
verification ladder T0 review T1 audit T2 compute T3 formal
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Modern data acquisition routinely produce massive amounts of event sequence data in various domains, such as social media, healthcare, and financial markets. These data often exhibit complicated short-term and long-term temporal dependencies. However, most of the existing recurrent neural network based point process models fail to capture such dependencies, and yield unreliable prediction performance. To address this issue, we propose a Transformer Hawkes Process (THP) model, which leverages the self-attention mechanism to capture long-term dependencies and meanwhile enjoys computational efficiency. Numerical experiments on various datasets show that THP outperforms existing models in terms of both likelihood and event prediction accuracy by a notable margin. Moreover, THP is quite general and can incorporate additional structural knowledge. We provide a concrete example, where THP achieves improved prediction performance for learning multiple point processes when incorporating their relational information.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 22 citations worldwide. Full citation record

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

    cs.LG 2025-09 conditional novelty 6.0 of 10

    A pretrained in-context transformer infers Hawkes-style conditional intensities from event histories and transfers zero-shot to real-world event data, roughly matching specialized models after finetuning.

  2. SenDaL: An Effective and Efficient Calibration Framework of Low-Cost Sensors for Daily Life

    cs.LG 2025-02 conditional novelty 4.0 of 10

    SenDaL trains a router to switch between a linear and a deep calibration model, achieving deep-model accuracy at near-linear-model speed on low-cost fine-dust sensors.

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