A Machine Learning approach for learning temporal point process

Andrija Petrović2, Aleksa Biserčić1, Boris Delibašić1 and Dimitrije Milenković1

  1. Faculty of Organizational Sciences University of Belgrade, Jove Ilica 154
    11000 Belgrade, Serbia
  2. Technical Faculty University Singidunum, Danijelova 32
    11000 Belgrade, Serbia


Despite a vast application of temporal point processes in infectious disease diffusion forecasting, ecommerce, traffic prediction, preventive maintenance, etc, there is no significant development in improving the simulation and prediction of temporal point processes in real-world environments. With this problem at hand, we propose a novel methodology for learning temporal point processes based on one-dimensional numerical integration techniques. These techniques are used for linearising the negative maximum likelihood (neML) function and enabling backpropagation of the neML derivatives. Our approach is tested on two real-life datasets. Firstly, on high frequency point process data, (prediction of highway traffic) and secondly, on a very low frequency point processes dataset, (prediction of ski injuries in ski resorts). Four different point process baseline models were compared: second-order Polynomial inhomogeneous process, Hawkes process with exponential kernel, Gaussian process, and Poisson process. The results show the ability of the proposed methodology to generalize on different datasets and illustrate how different numerical integration techniques and mathematical models influence the quality of the obtained models. The presented methodology is not limited to these datasets and can be further used to optimize and predict other processes that are based on temporal point processes.

Key words

temporal point process, Hawkes process, Poisson process, highway traffic prediction, ski injury prediction

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Publication information

Volume 19, Issue 2 (June 2022)
Year of Publication: 2022
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Petrović, A., Biserčić, A., Delibašić, B., Milenković, D.: A Machine Learning approach for learning temporal point process. Computer Science and Information Systems, Vol. 19, No. 2, 1007–1022. (2022), https://doi.org/10.2298/CSIS210609016P