eprintid: 1572407
rev_number: 36
eprint_status: archive
userid: 608
dir: disk0/01/57/24/07
datestamp: 2018-01-29 17:48:42
lastmod: 2021-11-15 02:59:47
status_changed: 2018-01-29 17:48:42
type: article
metadata_visibility: show
creators_name: Aste, T
creators_name: Di Matteo, T
title: Sparse Causality Network Retrieval from Short Time Series
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
note: Copyright © 2017 Tomaso Aste and T. Di Matteo. This is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
abstract: We investigate how efficiently a known underlying sparse causality structure of a simulated multivariate linear process can be retrieved from the analysis of time series of short lengths. Causality is quantified from conditional transfer entropy and the network is constructed by retaining only the statistically validated contributions. We compare results from three methodologies: two commonly used regularization methods, Glasso and ridge, and a newly introduced technique, LoGo, based on the combination of information filtering network and graphical modelling. For these three methodologies we explore the regions of time series lengths and model-parameters where a significant fraction of true causality links is retrieved. We conclude that when time series are short, with their lengths shorter than the number of variables, sparse models are better suited to uncover true causality links with LoGo retrieving the true causality network more accurately than Glasso and ridge.
date: 2017
date_type: published
publisher: WILEY-HINDAWI
official_url: http://dx.doi.org/10.1155/2017/4518429
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1298017
doi: 10.1155/2017/4518429
lyricists_name: Aste, Tomaso
lyricists_id: TASTE72
actors_name: Allington-Smith, Dominic
actors_id: DAALL44
actors_role: owner
full_text_status: public
publication: Complexity
volume: 2017
article_number: 4518429
pages: 13
issn: 1099-0526
citation:        Aste, T;    Di Matteo, T;      (2017)    Sparse Causality Network Retrieval from Short Time Series.                   Complexity , 2017     , Article 4518429.  10.1155/2017/4518429 <https://doi.org/10.1155/2017%2F4518429>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/1572407/7/4518429.pdf