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Sparse Causality Network Retrieval from Short Time Series
Joint Authors
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-11-06
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Aste, Tomaso& Di Matteo, T.. 2017. Sparse Causality Network Retrieval from Short Time Series. Complexity،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1142875
Modern Language Association (MLA)
Aste, Tomaso& Di Matteo, T.. Sparse Causality Network Retrieval from Short Time Series. Complexity No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1142875
American Medical Association (AMA)
Aste, Tomaso& Di Matteo, T.. Sparse Causality Network Retrieval from Short Time Series. Complexity. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1142875
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142875