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• W2891640452 abstract "Hawkes Processes capture self-excitation and mutual-excitation between events when the arrival of an event makes future events more likely to happen. Identification of such temporal covariance can reveal the underlying structure to better predict future events. In this paper, we present a new framework to decompose discrete events with a composition of multiple self-triggering kernels. The composition scheme allows us to decompose empirical covariance densities into the sum or the product of base kernels which are easily interpretable. Here, we present the first multiplicative kernel composition methods for Hawkes Processes. We demonstrate that the new automatic kernel decomposition procedure outperforms the existing methods on the prediction of discrete events in real-world data." @default.
• W2891640452 created "2018-09-27" @default.
• W2891640452 creator A5052985764 @default.
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• W2891640452 date "2017-03-27" @default.
• W2891640452 modified "2023-09-27" @default.
• W2891640452 title "Make Hawkes Processes Explainable by Decomposing Self-Triggering Kernels" @default.
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