Aste, T;
Di Matteo, T;
(2017)
Sparse Causality Network Retrieval from Short Time Series.
Complexity
, 2017
, Article 4518429. 10.1155/2017/4518429.
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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.
| Type: | Article |
|---|---|
| Title: | Sparse Causality Network Retrieval from Short Time Series |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1155/2017/4518429 |
| Publisher version: | http://dx.doi.org/10.1155/2017/4518429 |
| Language: | English |
| Additional information: | 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. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1572407 |
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