Detecting dynamical changes in time series by using the Jensen Shannon divergence

Mateos, Diego M.; Riveaud, Leonardo Esteban; Lamberti, Pedro W.

doi:10.1063/1.4999613

Cited by 19 publications

(15 citation statements)

References 42 publications

(33 reference statements)

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“…With the aim of achieving a better characterization regarding the significance of the results obtained with the PJSD, we have developed some comparative analysis with other two quantifiers of similarity/dissimilarity between signals; namely, the information-based similarity index (IBSI) [32,33] and the alphabetic Jensen-Shannon divergence (aJSD) [38]. A binary coarse graining depending on the relative amplitudes of successive values is first implemented in both of them and, then, binary sequences of length m (m-bit words) are obtained.…”

Section: Appendix: Performance Comparison Against Other Approachesmentioning

confidence: 99%

“…Much more recently, an approach based on the differences between Rényi entropy spectra has been implemented by Xu and Beck [14]. Finally, the alphabetic Jensen-Shannon divergence, a relative distance (in a distributional sense) between time series, has been introduced by Mateos et al [38] for detecting dynamical changes. A binary encoding is undertaken as a coarsegrained representation of the signals, and then used to estimate the Jensen-Shannon divergence.…”

Section: Introductionmentioning

confidence: 99%

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Permutation Jensen-Shannon distance: A versatile and fast symbolic tool for complex time series analysis

Zunino,

Olivares,

Ribeiro

et al. 2022

Preprint

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The main motivation of this paper is to introduce the permutation Jensen-Shannon distance, a symbolic tool able to quantify the degree of similarity between two arbitrary time series. This quantifier results from the fusion of two concepts, the Jensen-Shannon divergence and the encoding scheme based on the sequential ordering of the elements in the data series. The versatility and robustness of this ordinal symbolic distance for characterizing and discriminating different dynamics are illustrated through several numerical and experimental applications. Results obtained allow us to be optimistic about its usefulness in the field of complex time series analysis. Moreover, thanks to its simplicity, low computational cost, wide applicability and less susceptibility to outliers and artifacts, this ordinal measure can efficiently handle large amounts of data and help to tackle the current big data challenges.

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Section: Appendix: Performance Comparison Against Other Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Permutation Jensen-Shannon distance: A versatile and fast symbolic tool for complex time series analysis

Zunino,

Olivares,

Ribeiro

et al. 2022

Preprint

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“…We also normalize the number of observations in each bin by the total number of single pulses in each observation. For each of these fittings we perform a Jensen-Shannon divergence test (Mateos et al 2017) to quantify the difference between the empirical and the theoretical distribution. Table 2 gives the scintillation fit parameters of the amplitude versus the number of pulses (the insets in Figs.…”

Section: Scintillationsmentioning

confidence: 99%

Vela Pulsar: Single Pulses Analysis with Machine Learning Techniques

Lousto,

Missel,

Prajapati

et al. 2021

Preprint

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We study individual pulses of Vela (PSR B0833-45 / J0835-4510) from daily observations of over three hours (around 120,000 pulses per observation), performed simultaneously with the two radio telescopes at the Argentine Institute of Radioastronomy. We select 4 days of observations in January-March 2021 and study their statistical properties with machine learning techniques. We first use density based DBSCAN clustering techniques, associating pulses mainly by amplitudes, and find a correlation between higher amplitudes and earlier arrival times. We also find a weaker (polarization dependent) correlation with the mean width of the pulses. We identify clusters of the so-called mini-giant pulses, with ∼ 10× the average pulse amplitude. We then perform an independent study, with Self-Organizing Maps (SOM) clustering techniques. We use Variational AutoEncoder (VAE) reconstruction of the pulses to separate them clearly from the noise and select one of the days of observation to train VAE and apply it to thre rest of the observations. We use SOM to determine 4 clusters of pulses per day per radio telescope and conclude that our main results are robust and self-consistent. These results support models for emitting regions at different heights (separated each by roughly a hundred km) in the pulsar magnetosphere. We also model the pulses amplitude distribution with interstellar scintillation patterns at the inter-pulses time-scale finding a characterizing exponent 𝑛 ISS ∼ 7 − 10. In the appendices we discuss independent checks of hardware systematics with the simultaneous use of the two radio telescopes in different one-polarization / two-polarizations configurations. We also provide a detailed analysis of the processes of radio-interferences cleaning and individual pulse folding.

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“…The difference between sequences ι(s i , s j ) is calculated using the so-called ,,alphabetic Jensen-Shannon Distance" (aJSD, Mateos et al (2017)), which first discretizes timeseries data in a combined probability space, on which then the Jensen-Shannon Distance can be applied (see the algorithm description in eq. 6).…”

Section: Conflict Of Interest Statementmentioning

confidence: 99%

A predictive processing model of perception and action for self-other distinction

Kahl,

Kopp

2018

Preprint

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During interaction with others, we perceive and produce social actions in close temporal distance or even simultaneously. It has been argued that the motor system is involved in perception and action, playing a fundamental role in the handling of actions produced by oneself and by others. But how does it distinguish in this processing between self and other, thus contributing to self-other distinction? In this paper we propose a hierarchical model of sensorimotor coordination based on principles of perception-action coupling and predictive processing in which self-other distinction arises during action and perception. For this we draw on mechanisms assumed for the integration of cues for a sense of agency, i.e., the sense that an action is self-generated. We report results from simulations of different scenarios, showing that the model is not only able to minimize free energy during perception and action, but also showing that the model can correctly attribute sense of agency to own actions.

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Detecting dynamical changes in time series by using the Jensen Shannon divergence

Cited by 19 publications

References 42 publications

Permutation Jensen-Shannon distance: A versatile and fast symbolic tool for complex time series analysis

Permutation Jensen-Shannon distance: A versatile and fast symbolic tool for complex time series analysis

Vela Pulsar: Single Pulses Analysis with Machine Learning Techniques

A predictive processing model of perception and action for self-other distinction

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