Results 11 to 20 of about 54,062 (250)
Evaluating Methods for Detrending Time Series Using Ordinal Patterns, with an Application to Air Transport Delays [PDF]
EntropyFunctional networks have become a standard tool for the analysis of complex systems, allowing the unveiling of their internal connectivity structure while only requiring the observation of the system’s constituent dynamics.
Felipe Olivares +3 more
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Algorithmics, Possibilities and Limits of Ordinal Pattern Based Entropies [PDF]
Entropy, 2019 The study of nonlinear and possibly chaotic time-dependent systems involves long-term data acquisition or high sample rates. The resulting big data is valuable in order to provide useful insights into long-term dynamics.
Albert B. Piek +2 more
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Ordinal Patterns in Heartbeat Time Series: An Approach Using Multiscale Analysis [PDF]
Entropy, 2019 In this paper, we simultaneously use two different scales in the analysis of ordinal patterns to measure the complexity of the dynamics of heartbeat time series. Rényi entropy and weighted Rényi entropy are the entropy-like measures proposed in
María Muñoz-Guillermo
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Ordinal pattern-based change point detection. [PDF]
Test (Madr)Abstract The ordinal patterns of a fixed number of consecutive values in a time series are the spatial ordering of these values. Counting how often a specific ordinal pattern occurs in a time series provides important insights into the properties of the time series.
Betken A, Micali G, Schmidt-Hieber J.
europepmc +4 more sources
Ordinal analysis of lexical patterns
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2023 Words are fundamental linguistic units that connect thoughts and things through meaning. However, words do not appear independently in a text sequence. The existence of syntactic rules induces correlations among neighboring words. Using an ordinal pattern approach, we present an analysis of lexical statistical connections for 11 major languages.
David Sánchez +4 more
openaire +5 more sources
Permutation Entropy of Weakly Noise-Affected Signals
Entropy, 2021 We analyze the permutation entropy of deterministic chaotic signals affected by a weak observational noise. We investigate the scaling dependence of the entropy increase on both the noise amplitude and the window length used to encode the time series. In
Leonardo Ricci, Antonio Politi
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Unveiling the Connectivity of Complex Networks Using Ordinal Transition Methods
Entropy, 2023 Ordinal measures provide a valuable collection of tools for analyzing correlated data series. However, using these methods to understand information interchange in the networks of dynamical systems, and uncover the interplay between dynamics and ...
Juan A. Almendral +2 more
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Statistically Significant Pattern Mining with Ordinal Utility [PDF]
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020 Statistically significant patterns mining (SSPM) is an essential and challenging data mining task in the field of knowledge discovery in databases (KDD), in which each pattern is evaluated via a hypothesis test. Our study aims to introduce a preference relation into patterns and to discover the most preferred patterns under the constraint of ...
Thien Q. Tran +3 more
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Ordinal patterns in long‐range dependent time series [PDF]
Scandinavian Journal of Statistics, 2020 AbstractWe analyze the ordinal structure of long‐range dependent time series. To this end, we use so called ordinal patterns which describe the relative position of consecutive data points. We provide two estimators for the probabilities of ordinal patterns and prove limit theorems in different settings, namely stationarity and (less restrictive ...
Annika Betken +5 more
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Frontiers in Artificial Intelligence, 2021 Subtype and Stage Inference (SuStaIn) is an unsupervised learning algorithm that uniquely enables the identification of subgroups of individuals with distinct pseudo-temporal disease progression patterns from cross-sectional datasets.
Alexandra L. Young +15 more
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