16,046 research outputs found
Recurrence networks - A novel paradigm for nonlinear time series analysis
This paper presents a new approach for analysing structural properties of
time series from complex systems. Starting from the concept of recurrences in
phase space, the recurrence matrix of a time series is interpreted as the
adjacency matrix of an associated complex network which links different points
in time if the evolution of the considered states is very similar. A critical
comparison of these recurrence networks with similar existing techniques is
presented, revealing strong conceptual benefits of the new approach which can
be considered as a unifying framework for transforming time series into complex
networks that also includes other methods as special cases.
It is demonstrated that there are fundamental relationships between the
topological properties of recurrence networks and the statistical properties of
the phase space density of the underlying dynamical system. Hence, the network
description yields new quantitative characteristics of the dynamical complexity
of a time series, which substantially complement existing measures of
recurrence quantification analysis
Delay Parameter Selection in Permutation Entropy Using Topological Data Analysis
Permutation Entropy (PE) is a powerful tool for quantifying the
predictability of a sequence which includes measuring the regularity of a time
series. Despite its successful application in a variety of scientific domains,
PE requires a judicious choice of the delay parameter . While another
parameter of interest in PE is the motif dimension , Typically is
selected between and with or giving optimal results for the
majority of systems. Therefore, in this work we focus solely on choosing the
delay parameter. Selecting is often accomplished using trial and error
guided by the expertise of domain scientists. However, in this paper, we show
that persistent homology, the flag ship tool from Topological Data Analysis
(TDA) toolset, provides an approach for the automatic selection of . We
evaluate the successful identification of a suitable from our TDA-based
approach by comparing our results to a variety of examples in published
literature
Persistent Homology of Attractors For Action Recognition
In this paper, we propose a novel framework for dynamical analysis of human
actions from 3D motion capture data using topological data analysis. We model
human actions using the topological features of the attractor of the dynamical
system. We reconstruct the phase-space of time series corresponding to actions
using time-delay embedding, and compute the persistent homology of the
phase-space reconstruction. In order to better represent the topological
properties of the phase-space, we incorporate the temporal adjacency
information when computing the homology groups. The persistence of these
homology groups encoded using persistence diagrams are used as features for the
actions. Our experiments with action recognition using these features
demonstrate that the proposed approach outperforms other baseline methods.Comment: 5 pages, Under review in International Conference on Image Processin
Persistent Homology of Delay Embeddings
The objective of this study is to detect and quantify the periodic behavior
of the signals using topological methods. We propose to use delay-coordinate
embeddings as a tool to measure the periodicity of signals. Moreover, we use
persistent homology for analyzing the structure of point clouds of
delay-coordinate embeddings. A method for finding the appropriate value of
delay is proposed based on the autocorrelation function of the signals. We
apply this topological approach to wheeze signals by introducing a model based
on their harmonic characteristics. Wheeze detection is performed using the
first Betti numbers of a few number of landmarks chosen from embeddings of the
signals.Comment: 16 pages, 8 figure
Topological properties and fractal analysis of recurrence network constructed from fractional Brownian motions
Many studies have shown that we can gain additional information on time
series by investigating their accompanying complex networks. In this work, we
investigate the fundamental topological and fractal properties of recurrence
networks constructed from fractional Brownian motions (FBMs). First, our
results indicate that the constructed recurrence networks have exponential
degree distributions; the relationship between and of recurrence networks decreases with the Hurst
index of the associated FBMs, and their dependence approximately satisfies
the linear formula . Moreover, our numerical results of
multifractal analysis show that the multifractality exists in these recurrence
networks, and the multifractality of these networks becomes stronger at first
and then weaker when the Hurst index of the associated time series becomes
larger from 0.4 to 0.95. In particular, the recurrence network with the Hurst
index possess the strongest multifractality. In addition, the
dependence relationships of the average information dimension on the Hurst index can also be
fitted well with linear functions. Our results strongly suggest that the
recurrence network inherits the basic characteristic and the fractal nature of
the associated FBM series.Comment: 25 pages, 1 table, 15 figures. accepted by Phys. Rev.
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