SurF applies the Time Rescaling Theorem as a learnable bijection to create a single generative model for forecasting irregular multivariate event streams that outperforms or matches baselines on six benchmarks.
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process
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abstract
Many events occur in the world. Some event types are stochastically excited or inhibited---in the sense of having their probabilities elevated or decreased---by patterns in the sequence of previous events. Discovering such patterns can help us predict which type of event will happen next and when. We model streams of discrete events in continuous time, by constructing a neurally self-modulating multivariate point process in which the intensities of multiple event types evolve according to a novel continuous-time LSTM. This generative model allows past events to influence the future in complex and realistic ways, by conditioning future event intensities on the hidden state of a recurrent neural network that has consumed the stream of past events. Our model has desirable qualitative properties. It achieves competitive likelihood and predictive accuracy on real and synthetic datasets, including under missing-data conditions.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Separating presence from magnitude in sparse temporal audit data lets a dual-channel autoencoder focus learning on anomalous activity for insider threat detection.
citing papers explorer
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SurF: A Generative Model for Multivariate Irregular Time Series Forecasting
SurF applies the Time Rescaling Theorem as a learnable bijection to create a single generative model for forecasting irregular multivariate event streams that outperforms or matches baselines on six benchmarks.
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Signal Decomposition Reveals Structure in Insider Threat Detection under Sparse Temporal Data
Separating presence from magnitude in sparse temporal audit data lets a dual-channel autoencoder focus learning on anomalous activity for insider threat detection.