ICML 2017poster121 citations
Uncovering Causality from Multivariate Hawkes Integrated Cumulants
Massil Achab, Emmanuel Bacry, Stéphane Gaı̈ffas, Iacopo Mastromatteo, Jean-François Muzy
Abstract
We design a new nonparametric method that allows one to estimate the matrix of integrated kernels of a multivariate Hawkes process. This matrix not only encodes the mutual influences of each node of the process, but also disentangles the causality relationships between them. Our approach is the first that leads to an estimation of this matrix
BibTeX
@InProceedings{pmlr-v70-achab17a,
title = {Uncovering Causality from Multivariate {H}awkes Integrated Cumulants},
author = {Massil Achab and Emmanuel Bacry and St{\'e}phane Ga\"{\i}ffas and Iacopo Mastromatteo and Jean-Fran{\c{c}}ois Muzy},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {1--10},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/achab17a/achab17a.pdf},
url = {https://proceedings.mlr.press/v70/achab17a.html},
abstract = {We design a new nonparametric method that allows one to estimate the matrix of integrated kernels of a multivariate Hawkes process. This matrix not only encodes the mutual influences of each node of the process, but also disentangles the causality relationships between them. Our approach is the first that leads to an estimation of this matrix