Using permutations to detect dependence between time series

Cánovas, José S.; Guillamón, Antonio; Ruiz, Manuel

doi:10.1016/j.physd.2011.04.010

Cited by 20 publications

(13 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cánovas et al [50] proposed an alternative approach for the analysis of the dependence of two time series, based on the construction of contingency tables, i.e., matrices where the frequency of co-appearance of two patterns in two different time series at the same time is reported. Once a contingency table has been constructed, the independence of both series can be checked with standard statistical tests, including Pearson's chi-square, G-test, or the Fisher-Freeman-Halton test [51].…”

Section: Dependences Between Time Seriesmentioning

confidence: 99%

Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review

Zanin

Zunino

Rosso

et al. 2012

Entropy

554

440

View full text Add to dashboard Cite

Entropy is a powerful tool for the analysis of time series, as it allows describing the probability distributions of the possible state of a system, and therefore the information encoded in it. Nevertheless, important information may be codified also in the temporal dynamics, an aspect which is not usually taken into account. The idea of calculating entropy based on permutation patterns (that is, permutations defined by the order relations among values of a time series) has received a lot of attention in the last years, especially for the understanding of complex and chaotic systems. Permutation entropy directly accounts for the temporal information contained in the time series; furthermore, it has the quality of simplicity, robustness and very low computational cost. To celebrate the tenth anniversary of the original work, here we analyze the theoretical foundations of the permutation entropy, Entropy 2012, 14 1554 as well as the main recent applications to the analysis of economical markets and to the understanding of biomedical systems.

show abstract

Section: Dependences Between Time Seriesmentioning

confidence: 99%

Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review

Zanin

Zunino

Rosso

et al. 2012

Entropy

554

440

View full text Add to dashboard Cite

show abstract

“…Some common statistics in the context of contingency tables are Pearson's chi-square, the likelihood ratio and the Cressi-Read statistics, which are used in [11] to test the independency between two time series. That paper also shows the efficiency of the method in detecting linear and nonlinear dependence.…”

Section: Similarity and Distance Measures Based On Permutationsmentioning

confidence: 99%

“…In our context, after the codifying process of the time series, we define the distance measures between two time series as follows: (22) and: (23) Note that, taking into account the nature of the time series codifying process through permutations, the distance measurements between two time series defined in (11), (19), (22) and (23) have the capability to detect linear and nonlinear dependencies (see [11] for more details).…”

Section: Similarity and Distance Measures Based On Permutationsmentioning

confidence: 99%

“…For example, [9,10] propose statistical tests for independence between two stationary time series, based on the residual cross-correlation. Later, [11] introduced an alternative test using symbolic dynamics through permutations, which is able to detect linear and nonlinear dependencies. The permutation entropy, also known as the Shannon permutation entropy, was introduced by [12] to study the complexity of a time series, and it has been widely used to determine the complexity changes of biological time series; see [13,14], among others.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dependency-Aware Clustering of Time Series and Its Application on Energy Markets

2016

Self Cite

View full text Add to dashboard Cite

Abstract:In this paper, we propose a novel approach for clustering time series, which combines three well-known aspects: a permutation-based coding of the time series, several distance measurements for discrete distributions and hierarchical clustering using different linkages. The proposed method classifies a set of time series into homogeneous groups, according to the degree of dependency among them. That is, time series with a high level of dependency will lie in the same cluster. Moreover, taking into account the nature of the codifying process, the method allows us to detect linear and nonlinear dependences. To illustrate the procedure, a set of fourteen electricity price series coming from different wholesale electricity markets worldwide was analyzed. We show that the classification results are consistent with the characteristics of the electricity markets in the study and with their degree of integration. Besides, we outline the necessity of removing the seasonal component of the price series before the analysis and the capability of the method to detect changes in the dependence level along time.

show abstract

“…It means that the dissimilar objects can be removed in advance through executing the algorithm of lower-bound function when applied to similarity search. The typical methods include parameter representation [18], piecewise aggregate approximation (PAA) [19], symbolic aggregate approximation (SAX) [7], piecewise linear approximation (PLA) [20], segmentation representation [21], piecewise vector quantized approximation [22] and so on. Especially, PAA is one of the most important methods to reduce the dimensionality of time series.…”

Section: Introductionmentioning

confidence: 99%

Piecewise aggregate representations and lower-bound distance functions for multivariate time series

2015

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

Using permutations to detect dependence between time series

Cited by 20 publications

References 15 publications

Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review

Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review

Dependency-Aware Clustering of Time Series and Its Application on Energy Markets

Piecewise aggregate representations and lower-bound distance functions for multivariate time series

Contact Info

Product

Resources

About