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Transfer entropy computation using the Perron-Frobenius operator

Diego, David; Haaga, Kristian Agasøster; Hannisdal, Bjarte
Peer reviewed, Journal article
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URI
https://hdl.handle.net/1956/21882
Date
2019
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  • Department of Earth Science [629]
Original version
Diego D, Haaga KA, Hannisdal B. Transfer entropy computation using the Perron-Frobenius operator. Physical review. E. 2019;99(4): 042212   https://doi.org/10.1103/physreve.99.042212
Abstract
We propose a method for computing the transfer entropy between time series using Ulam's approximation of the Perron-Frobenius (transfer) operator associated with the map generating the dynamics. Our method differs from standard transfer entropy estimators in that the invariant measure is estimated not directly from the data points, but from the invariant distribution of the transfer operator approximated from the data points. For sparse time series and low embedding dimension, the transfer operator is approximated using a triangulation of the attractor, whereas for data-rich time series or higher embedding dimension, we use a faster grid approach. We compare the performance of our methods with existing estimators such as the k nearest neighbors method and kernel density estimation method, using coupled instances of well known chaotic systems: coupled logistic maps and a coupled Rössler-Lorenz system. We find that our estimators are robust against moderate levels of noise. For sparse time series with less than 100 observations and low embedding dimension, our triangulation estimator shows improved ability to detect coupling directionality, relative to standard transfer entropy estimators.
Publisher
APS
Journal
Physical review. E
Copyright
Copyright 2019 American Physical Society

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