Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

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

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Journal of Machine Learning Research

ISSN

1532-4435

e-ISSN

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Svazek periodika

25

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

26

Strana od-do

133

Kód UT WoS článku

001230544600001

EID výsledku v databázi Scopus

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