Uncovering Causality from Multivariate Hawkes Integrated Cumulants

Authors: Massil Achab, Emmanuel Bacry, Stéphane Gaı̈ffas, Iacopo Mastromatteo, Jean-François Muzy

ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We show on numerical experiments that our approach is indeed very robust to the shape of the kernels, and gives appealing results on the Meme Tracker database and on financial order book data.
Researcher Affiliation Collaboration 1Ecole Polytechnique, Palaiseau, France 2Capital Fund Management, Paris, France 3Université de Corse, Corte, France.
Pseudocode Yes Algorithm 1 Non Parametric Hawkes Cumulant method
Open Source Code Yes An efficient implementation of this algorithm with Tensor Flow, see (Abadi et al., 2016), is available on Git Hub: https://github.com/achab/nphc.
Open Datasets Yes Meme Tracker dataset. We use events of the most active sites from the Meme Tracker dataset2. This dataset contains the publication times of articles in many websites/blogs from August 2008 to April 2009, and hyperlinks between posts. ... 2https://www.memetracker.org/data.html
Dataset Splits No The paper mentions using simulated, Meme Tracker, and financial datasets but does not provide specific details on training, validation, or test splits (percentages, counts, or references to predefined splits).
Hardware Specification No The paper mentions 'We ran multiprocessed versions of the baseline methods on 56 cores, to decrease the computing time.' but does not specify CPU model, memory, or any GPU details.
Software Dependencies No The paper mentions 'Tensor Flow' and 'tick' library but does not provide specific version numbers for these software components.
Experiment Setup Yes We used Ada Grad (Duchi et al., 2011)... In our setting, this algorithm gave nice convergence results for O = Id. ... We used M = 10 basis functions for both ODE and GC algorithms, and L = 50 quadrature points for WH. ... We performed the ADM4 estimation, with exponential kernel, by giving the exact value β = β0 of one block.