Sparse Causality Network Retrieval from Short Time Series

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 s...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2017-01, Vol.2017 (2017), p.1-13
Main Authors: Aste, Tomaso, Di Matteo, T.
Format: Article
Language:English
Subjects:
Analysis
Causality
Computer simulation
Filtration
Generalized linear models
Hypotheses
Information storage and retrieval
Regularization
Scientific method
Securities markets
Simulation
Time series
Time-series analysis
Traveling salesman problem
Values
Variables
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Summary: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.
ISSN:1076-2787
1099-0526
DOI:10.1155/2017/4518429