A NOVEL APPROACH FOR LEARNING TEMPORAL POINT PROCESS
Апстракт
In this paper, we presented a novel methodology for learning temporal point process based on the
implementation of one-dimensional numerical integration techniques. The implementation of numerical
methodology is used for linearizing negative maximum likelihood (neML) function to enable backpropagation
of neML derivative. The presented approach is tested on highway toll dataset. Moreover, four different wellknown
point process baseline models were compared: first-order and second-order polynomial Poisson
inhomogeneous process and Hawkes with exponential and Gaussian kernel. The results showed that different
numerical integration techniques influence the quality of the obtained models.
Кључне речи:
traffic prediction / temporal point process / Hawkes process / Poisson process / numerical integrationИзвор:
XVII International Symposium Business and Artificial Intelligence - SYMORG 2020, September 7-9, 2020, 327-333Издавач:
- University of Belgrade - Faculty of Organizational Sciences
Колекције
Институција/група
Mašinski fakultetTY - CONF AU - Milenković, Dimitrije AU - Petrović, Andrija AU - Bugarić, Uglješa PY - 2020 UR - https://machinery.mas.bg.ac.rs/handle/123456789/5839 AB - In this paper, we presented a novel methodology for learning temporal point process based on the implementation of one-dimensional numerical integration techniques. The implementation of numerical methodology is used for linearizing negative maximum likelihood (neML) function to enable backpropagation of neML derivative. The presented approach is tested on highway toll dataset. Moreover, four different wellknown point process baseline models were compared: first-order and second-order polynomial Poisson inhomogeneous process and Hawkes with exponential and Gaussian kernel. The results showed that different numerical integration techniques influence the quality of the obtained models. PB - University of Belgrade - Faculty of Organizational Sciences C3 - XVII International Symposium Business and Artificial Intelligence - SYMORG 2020, September 7-9 T1 - A NOVEL APPROACH FOR LEARNING TEMPORAL POINT PROCESS EP - 333 SP - 327 UR - https://hdl.handle.net/21.15107/rcub_machinery_5839 ER -
@conference{ author = "Milenković, Dimitrije and Petrović, Andrija and Bugarić, Uglješa", year = "2020", abstract = "In this paper, we presented a novel methodology for learning temporal point process based on the implementation of one-dimensional numerical integration techniques. The implementation of numerical methodology is used for linearizing negative maximum likelihood (neML) function to enable backpropagation of neML derivative. The presented approach is tested on highway toll dataset. Moreover, four different wellknown point process baseline models were compared: first-order and second-order polynomial Poisson inhomogeneous process and Hawkes with exponential and Gaussian kernel. The results showed that different numerical integration techniques influence the quality of the obtained models.", publisher = "University of Belgrade - Faculty of Organizational Sciences", journal = "XVII International Symposium Business and Artificial Intelligence - SYMORG 2020, September 7-9", title = "A NOVEL APPROACH FOR LEARNING TEMPORAL POINT PROCESS", pages = "333-327", url = "https://hdl.handle.net/21.15107/rcub_machinery_5839" }
Milenković, D., Petrović, A.,& Bugarić, U.. (2020). A NOVEL APPROACH FOR LEARNING TEMPORAL POINT PROCESS. in XVII International Symposium Business and Artificial Intelligence - SYMORG 2020, September 7-9 University of Belgrade - Faculty of Organizational Sciences., 327-333. https://hdl.handle.net/21.15107/rcub_machinery_5839
Milenković D, Petrović A, Bugarić U. A NOVEL APPROACH FOR LEARNING TEMPORAL POINT PROCESS. in XVII International Symposium Business and Artificial Intelligence - SYMORG 2020, September 7-9. 2020;:327-333. https://hdl.handle.net/21.15107/rcub_machinery_5839 .
Milenković, Dimitrije, Petrović, Andrija, Bugarić, Uglješa, "A NOVEL APPROACH FOR LEARNING TEMPORAL POINT PROCESS" in XVII International Symposium Business and Artificial Intelligence - SYMORG 2020, September 7-9 (2020):327-333, https://hdl.handle.net/21.15107/rcub_machinery_5839 .