THAP: A Matlab Toolkit for Learning with Hawkes Processes

August 28, 2017 Β· Entered Twilight Β· πŸ› arXiv.org

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Repo contents: Analysis, BasicFunc, Data, LICENSE, Learning, README.md, Simulation, Test_Causality.m, Test_Clustering.m, Test_Learning_HP_ArbitraryKernel.m, Test_Learning_HP_ExpKernel.m, Test_Learning_TVHP_SyntheticData.m, Test_MLE_SHP.m, Test_Simulation_ArbitraryKernel.m, Test_Simulation_ExpKernel.m, Test_Simulation_Runtime.m, Test_Simulation_TVHP.m, Test_batchVSonline.m, Test_superpose_syn_K_WLS_D5.m, Visualization, setup.m

Authors Hongteng Xu, Hongyuan Zha arXiv ID 1708.09252 Category stat.ML: Machine Learning (Stat) Cross-listed cs.LG Citations 13 Venue arXiv.org Repository https://github.com/HongtengXu/Hawkes-Process-Toolkit ⭐ 116 Last Checked 1 month ago
Abstract
As a powerful tool of asynchronous event sequence analysis, point processes have been studied for a long time and achieved numerous successes in different fields. Among various point process models, Hawkes process and its variants attract many researchers in statistics and computer science these years because they capture the self- and mutually-triggering patterns between different events in complicated sequences explicitly and quantitatively and are broadly applicable to many practical problems. In this paper, we describe an open-source toolkit implementing many learning algorithms and analysis tools for Hawkes process model and its variants. Our toolkit systematically summarizes recent state-of-the-art algorithms as well as most classic algorithms of Hawkes processes, which is beneficial for both academical education and research. Source code can be downloaded from https://github.com/HongtengXu/Hawkes-Process-Toolkit.
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