Accelerating Hawkes Process for Modelling Event History Data

Ram, Ashwin and Srijith, P K (2017) Accelerating Hawkes Process for Modelling Event History Data. In: ICML 2017 Time Series Workshop, Sydney, Australia.

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Hawkes Processes are probabilistic models use- ful for modelling the occurrences of events over time. They exhibit mutual excitation property, where a past event influences future events. This has been successful in modelling the evolution of memes and user behaviour in social net- works. In the Hawkes process, the occurrences of events are determined by an underlying inten- sity function which considers the influence from past events. The intensity function models the mutual-exciting nature by adding up the influ- ence from past events. The calculation of the in- tensity function for every new event requires time proportional to the number of past events. When the number of events is high, the repeated in- tensity function calculation will become expen- sive. We develop a faster approach which takes only constant time complexity to calculate the in- tensity function for every new event in a mutu- ally exciting Hawkes process. This is achieved by developing a recursive formulation for mutu- ally exciting Hawkes process and maintaining an additional data structure which takes a constant space. We found considerable improvement in runtime performance of the Hawkes process ap- plied to the sequential stance classification task on synthetic and real world datasets.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Conference or Workshop Item (Paper)
Subjects: Computer science > Computer programming, programs, data
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 29 Aug 2017 09:30
Last Modified: 29 Aug 2017 09:30
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