Stabilisation by noise on the boundary for a Chafee-Infante equation with dynamical boundary conditions
Detecting coupling directions with transcript mutual information: A comparative study
José M. Amigó 1,, , Beata Graff 2, , Grzegorz Graff 3, , Roberto Monetti 4, and Katarzyna Tessmer 5,
Centro de Investigación Operativa, Universidad Miguel Hernández, 03202 Elche, Spain
Department of Hypertension and Diabetology, Medical University of Gdańsk, 80-952 Gdańsk, Poland
Faculty of Applied Physics and Mathematics, Gdańsk University of Technology, 80-233 Gdańsk, Poland
IngSoft GmbH. Irrerstrasse 17. 90403 Nuernberg, Germany
* Corresponding author: jm.amigo@umh.es
Dedicated to Peter E. Kloeden on the occasion of his 70th birthday
Received March 2018 Revised July 2018 Published February 2019
Fund Project: J.M.A. was financially supported by the Spanish Ministry of Economy, Industry and Competitivity, grant MTM2016-74921-P (AEI/FEDER, EU). B.G. was financially supported by the National Science Centre, Poland, grant MAESTRO UMO-2011/02/A/NZ5/00329. G.G. was financially supported by the National Science Centre, Poland, grant UMO-2014/15/B/ST1/01710
Figure(5)
Causal relationships are important to understand the dynamics of coupled processes and, moreover, to influence or control the effects by acting on the causes. Among the different approaches to determine cause-effect relationships and, in particular, coupling directions in interacting random or deterministic processes, we focus in this paper on information-theoretic measures. So, we study in the theoretical part the difference between directionality indicators based on transfer entropy as well as on its dimensional reduction via transcripts in algebraic time series representations. In the applications we consider specifically the lowest dimensional case, i.e., 3-dimensional transfer entropy, which is currently one of the most popular causality indicators, and the (2-dimensional) mutual information of transcripts. Needless to say, the lower dimensionality of the transcript-based indicator can make a difference in practice, where datasets are usually small. To compare numerically the performance of both directionality indicators, synthetic data (obtained with random processes) and real world data (in the form of biomedical recordings) are used. As happened in previous related work, we found again that the transcript mutual information performs as good as, and in some cases even better than, the lowest dimensional binned and symbolic transfer entropy, the symbols being ordinal patterns.
Keywords: Information directionality, transfer entropy, time series analysis, algebraic symbolic representations, transcripts, dimensional reduction.
Mathematics Subject Classification: Primary: 37M10, 94A17; Secondary: 60G10.
Citation: José M. Amigó, Beata Graff, Grzegorz Graff, Roberto Monetti, Katarzyna Tessmer. Detecting coupling directions with transcript mutual information: A comparative study. Discrete & Continuous Dynamical Systems - B, 2019, 24 (8) : 4079-4097. doi: 10.3934/dcdsb.2019051
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Figure 1. Plots of $ \Delta AT_{\mathbf{\hat{Z}}\rightarrow \mathbf{\hat{Y}}}(\Lambda ) $ (solid line) and $ \Delta TI_{\mathbf{\hat{Z}}\rightarrow \mathbf{\hat{Y}}}(\Lambda ) $ (dash-dotted line) vs $ \Lambda $, $ 1\leq \Lambda \leq T-1 $, for $ T = 3,4,...,10 $
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Figure 2. Plots of $ \Delta AT_{\mathbf{\hat{Y}}\rightarrow \mathbf{\hat{X}}}(\Lambda ) $ (solid line) and $ \Delta TI_{\mathbf{\hat{Y}}\rightarrow \mathbf{\hat{X}}}(\Lambda ) $ (dash-dotted line) vs $ \Lambda $, $ 1\leq \Lambda \leq T-1 $, for $ T = 3,4,...,10 $
Figure 3. Plots of $ \Delta AT_{\mathbf{\hat{Z}}\rightarrow \mathbf{\hat{X}}}(\Lambda ) $ (solid line) and $ \Delta TI_{\mathbf{\hat{Z}}\rightarrow \mathbf{\hat{X}}}(\Lambda ) $ (dash-dotted line) vs $ \Lambda $, $ 1\leq \Lambda \leq T-1 $, for $ T = 3,4,...,10 $
Figure 4. Plots of $ \Delta AT_{\mathbf{\hat{D}}\rightarrow \mathbf{\hat{R}}}(\Lambda ) $ (continuous lines) and $ \Delta TI_{\mathbf{\hat{D}}\rightarrow \mathbf{\hat{R}}}(\Lambda ) $ (dash-dotted lines) for $ T = 8 $, $ 1\leq \Lambda \leq 7 $, and $ k_{zy} = 0.0 $ (first column), $ k_{zy} = 0.1 $ (second column), $ k_{zy} = 0.5 $ (third column), and $ k_{zy} = 0.9 $ (fourth column). Top row corresponds to the coupling direction $ \mathbf{\hat{Z}}\rightarrow \mathbf{\hat{Y}} $, middle row to $ \mathbf{\hat{Z}}\rightarrow \mathbf{\hat{X}} $, and bottom row to $ \mathbf{\hat{Y}}\rightarrow \mathbf{\hat{X}} $
Figure 5. The directionality indicators $ \Delta TE_{RR\rightarrow BP}(\Lambda ) $ (top row), $ \Delta STE_{RR\rightarrow BP}(\Lambda ) $ (middle row), and $ \Delta TMI_{RR\rightarrow BP}(\Lambda ) $ (bottom row) for the patient Groups Ⅰ (left column), Ⅱ (middle column), and ⅡB (right column) with $ T = 6 $ and $ 1\leq \Lambda \leq 5 $. For convenience, here $ TE $ stands for binned transfer entropy, $ STE $ for symbolic transfer entropy, and $ TMI $ for transcript mutual information. See the text for more details, the description of the patient Groups, and the data $ (RR_{n}) $ and $ (BP_{n}) $
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José M. Amigó Beata Graff Grzegorz Graff Roberto Monetti Katarzyna Tessmer
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