71 citations · 124 across the 4 of their papers we have counts for
7 papers
Compression-Complexity with Ordinal Patterns for Robust Causal Inference in Irregularly-Sampled Time Series
Aditi Kathpalia, Pouya Manshour, Milan Paluš
Distinguishing cause from effect is a scientific challenge resisting solutions from mathematics, statistics, information theory and computer science. Compression-Complexity Causali…
Linked by dynamics: wavelet--based mutual information rate as a connectivity measure and scale-specific networks
Milan Palus
Experimentally observed networks of interacting dynamical systems are inferred from recorded multivariate time series by evaluating a statistical measure of dependence, usually the…
Non-linear dependence and teleconnections in climate data: sources, relevance, nonstationarity
Jaroslav Hlinka, David Hartman, Martin Vejmelka +2
Quantification of relations between measured variables of interest by statistical measures of dependence is a common step in analysis of climate data. The term "connectivity" is us…
Small-world topology of functional connectivity in randomly connected dynamical systems
Jaroslav Hlinka, David Hartman, Milan Paluš
Characterization of real-world complex systems increasingly involves the study of their topological structure using graph theory. Among global network properties, small-world prope…
Coarse-grained entropy rates for characterization of complex time series
Milan Palus
A method for classification of complex time series using coarse-grained entropy rates (CER's) is presented. The CER's, which are computed from information-theoretic functionals --…
Estimating Predictability: Redundancy and Surrogate Data Method
M. Paluš, L. Pecen, D. Pivka
A method for estimating theoretical predictability of time series is presented, based on information-theoretic functionals---redundancies and surrogate data technique. The redundan…