Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F24%3A00602952" target="_blank" >RIV/67985807:_____/24:00602952 - isvavai.cz</a>

Result on the web

<a href="https://www.jmlr.org/papers/v25/23-1066.html" target="_blank" >https://www.jmlr.org/papers/v25/23-1066.html</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length

Original language description

Multivariate Hawkes processes (MHPs) are versatile probabilistic tools used to model various real -life phenomena: earthquakes, operations on stock markets, neuronal activity, virus propagation and many others. In this paper, we focus on MHPs with exponential decay kernels and estimate connectivity graphs, which represent the Granger causal relations between their components. We approach this inference problem by proposing an optimization criterion and model selection algorithm based on the minimum message length (MML) principle. MML compares Granger causal models using the Occam's razor principle in the following way: even when models have a comparable goodness -of -fit to the observed data, the one generating the most concise explanation of the data is preferred. While most of the state -of -art methods using lasso -type penalization tend to overfitting in scenarios with short time horizons, the proposed MML-based method achieves high F1 scores in these settings. We conduct a numerical study comparing the proposed algorithm to other related classical and state -of -art methods, where we achieve the highest F1 scores in specific sparse graph settings. We illustrate the proposed method also on G7 sovereign bond data and obtain causal connections, which are in agreement with the expert knowledge available in the literature.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

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

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Name of the periodical

Journal of Machine Learning Research

ISSN

1532-4435

e-ISSN

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Volume of the periodical

25

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

26

Pages from-to

133

UT code for WoS article

001230544600001

EID of the result in the Scopus database

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