Poisson Graphical Granger Causality by Minimum Message Length

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00539725" target="_blank" >RIV/67985807:_____/21:00539725 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-030-67658-2_30" target="_blank" >http://dx.doi.org/10.1007/978-3-030-67658-2_30</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-67658-2_30" target="_blank" >10.1007/978-3-030-67658-2_30</a>

Result language

angličtina

Original language name

Poisson Graphical Granger Causality by Minimum Message Length

Original language description

Graphical Granger models are popular models for causal inference among time series. In this paper we focus on the Poisson graphical Granger model where the time series follow Poisson distribution. We use minimum message length principle for determination of causal connections in the model. Based on the dispersion coefficient of each time series and on the initial maximum likelihood estimates of the regression coefficients, we propose a minimum message length criterion to select the subset of causally connected time series with each target time series. We propose a genetic-type algorithm to find this set. To our best knowledge, this is the first work on applying the minimum message length principle to the Poisson graphical Granger model. Common graphical Granger models are usually applied in scenarios when the number of time observations is much greater than the number of time series, normally by several orders of magnitude. In the opposite case of “short” time series, these methods often suffer from overestimation. We demonstrate in the experiments with synthetic Poisson and point process time series that our method is for short time series superior in precision to the compared causal inference methods, i.e. the heterogeneous Granger causality method, the Bayesian causal inference method using structural equation models LINGAM and the point process Granger causality.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10102 - Applied mathematics

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

2021

Confidentiality

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

Article name in the collection

Machine Learning and Knowledge Discovery in Databases. Proceedings, Part 1

ISBN

978-3-030-67657-5

ISSN

0302-9743

e-ISSN

—

Number of pages

16

Pages from-to

526-541

Publisher name

Springer

Place of publication

Cham

Event location

Ghent / Virtual

Event date

Sep 14, 2020

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

000717522300030