Causal Discovery in Hawkes Processes by Minimum Description Length

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F22%3A00569861" target="_blank" >RIV/67985807:_____/22:00569861 - isvavai.cz</a>

Result on the web

<a href="https://ojs.aaai.org/index.php/AAAI/article/view/20656/20415" target="_blank" >https://ojs.aaai.org/index.php/AAAI/article/view/20656/20415</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Causal Discovery in Hawkes Processes by Minimum Description Length

Original language description

Hawkes processes are a special class of temporal point processes which exhibit a natural notion of causality, as occurrence of events in the past may increase the probability of events in the future. Discovery of the underlying infuence network among the dimensions of multi-dimensional temporal processes is of high importance in disciplines where a high-frequency data is to model, e.g. in fnancial data or in seismological data. This paper approaches the problem of learning Granger-causal network in multi-dimensional Hawkes processes. We formulate this problem as a model selection task in which we follow the minimum description length (MDL) principle. Moreover, we propose a general algorithm for MDL-based inference using a Monte-Carlo method and we use it for our causal discovery problem. We compare our algorithm with the state-of-the-art baseline methods on synthetic and real-world fnancial data. The synthetic experiments demonstrate superiority of our method in causal graph discovery compared to the baseline methods with respect to the size of the data. The results of experiments with the G-7 bonds price data are consistent with the experts’ knowledge.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA19-16066S" target="_blank" >GA19-16066S: Nonlinear interactions and information transfer in complex systems with extreme events</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 36th AAAI Conference on Artificial Intelligence

ISBN

978-1-57735-876-3

ISSN

2159-5399

e-ISSN

2374-3468

Number of pages

10

Pages from-to

6978-6987

Publisher name

AAAI Press

Place of publication

Palo Alto

Event location

Online

Event date

Feb 22, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000893636207010