TY - JOUR
T1 - Estimating conditional transfer entropy in time series using mutual information and nonlinear prediction
AU - Baboukani, Payam Shahsavari
AU - Graversen, Carina
AU - Alickovic, Emina
AU - Østergaard, Jan
N1 - Funding Information:
Funding: This research was partially funded by Centre for Acoustic Signal Processing Research (CASPR).
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/10
Y1 - 2020/10
N2 - We propose a new estimator to measure directed dependencies in time series. The dimensionality of data is first reduced using a new non-uniform embedding technique, where the variables are ranked according to a weighted sum of the amount of new information and improvement of the prediction accuracy provided by the variables. Then, using a greedy approach, the most informative subsets are selected in an iterative way. The algorithm terminates, when the highest ranked variable is not able to significantly improve the accuracy of the prediction as compared to that obtained using the existing selected subsets. In a simulation study, we compare our estimator to existing state-of-the-art methods at different data lengths and directed dependencies strengths. It is demonstrated that the proposed estimator has a significantly higher accuracy than that of existing methods, especially for the difficult case, where the data are highly correlated and coupled. Moreover, we show its false detection of directed dependencies due to instantaneous couplings effect is lower than that of existing measures. We also show applicability of the proposed estimator on real intracranial electroencephalography data.
AB - We propose a new estimator to measure directed dependencies in time series. The dimensionality of data is first reduced using a new non-uniform embedding technique, where the variables are ranked according to a weighted sum of the amount of new information and improvement of the prediction accuracy provided by the variables. Then, using a greedy approach, the most informative subsets are selected in an iterative way. The algorithm terminates, when the highest ranked variable is not able to significantly improve the accuracy of the prediction as compared to that obtained using the existing selected subsets. In a simulation study, we compare our estimator to existing state-of-the-art methods at different data lengths and directed dependencies strengths. It is demonstrated that the proposed estimator has a significantly higher accuracy than that of existing methods, especially for the difficult case, where the data are highly correlated and coupled. Moreover, we show its false detection of directed dependencies due to instantaneous couplings effect is lower than that of existing measures. We also show applicability of the proposed estimator on real intracranial electroencephalography data.
KW - Conditional transfer entropy
KW - Directed dependency
KW - Mutual information
KW - Non-uniform embedding
KW - Nonlinear prediction
UR - http://www.scopus.com/inward/record.url?scp=85092799134&partnerID=8YFLogxK
U2 - 10.3390/e22101124
DO - 10.3390/e22101124
M3 - Journal article
AN - SCOPUS:85092799134
SN - 1099-4300
VL - 22
SP - 1
EP - 21
JO - Entropy
JF - Entropy
IS - 10
M1 - 1124
ER -