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Transformer Hawkes Process

About

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.

Simiao Zuo, Haoming Jiang, Zichong Li, Tuo Zhao, Hongyuan Zha• 2020

Related benchmarks

TaskDatasetResultRank
Event PredictionStackOverflow
RMSE0.973
42
Event PredictionRetweet
RMSE22.32
18
Event Forecastingtaxi
RMSE0.369
16
Event PredictionMIMIC
Accuracy74.1
15
Next event predictionAMAZON
RMSE0.612
14
Event Sequence ForecastingPUBG (test)
Wasserstein Distance0.04
13
Event Sequence ForecastingReddit-C (test)
Wasserstein distance0.08
13
Event Sequence ForecastingReddit-S (test)
Wasserstein Distance0.11
13
Event Sequence ForecastingTwitter (test)
Wasserstein Distance0.05
13
Event Sequence ForecastingYelp1 (test)
Wasserstein Distance0.11
13
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