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6 edition of Multiscale Analysis of Complex Time Series found in the catalog.

Multiscale Analysis of Complex Time Series

Integration of Chaos and Random Fractal Theory, and Beyond

by Jianbo Gao

  • 217 Want to read
  • 1 Currently reading

Published by Wiley-Interscience .
Written in English


The Physical Object
Number of Pages352
ID Numbers
Open LibraryOL7619718M
ISBN 100471654701
ISBN 109780471654704


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Multiscale Analysis of Complex Time Series by Jianbo Gao Download PDF EPUB FB2

Multiscale Analysis of Complex Time Series fills this pressing need by presenting chaos and random fractal theory in a unified manner. Adopting a data-driven approach, the book covers: DNA sequence analysis. EEG analysis. Heart rate variability analysis.

Neural information by:   Multiscale Analysis of Complex Time Series fills this pressing need by presenting chaos and random fractal theory in a unified manner.

Adopting a data-driven approach, the book covers: DNA sequence analysis; EEG analysis; Heart rate variability analysis; Neural information processing; Network traffic modeling. Buy Multiscale Analysis of Complex Time Series: Integration of Chaos and Random Fractal Theory, and Beyond 1st edition by Gao, Jianbo, Cao, Yinhe, Tung, Wen-wen, Hu, Jing () Hardcover on FREE SHIPPING on qualified orders5/5(5).

After spending much time in looking for a good book about multiscale analysis of time series, I found this wonderful book by Jianbo Gao and his colleagues.

The book summarizes the current approaches to investigate complex time series analysis, with many applications to real world data.5/5.

Buy Multiscale Analysis of Complex Time Series: Integration of Chaos and Random Fractal Theory, and Beyond by Gao, Jianbo, Cao, Yinhe, Tung, Wen–wen, Hu, Jing (ISBN: ) from Amazon's Book Store.

Everyday low prices and free delivery on eligible orders.5/5(3). Multiscale Analysis of Complex Time Series fills this pressing need by presenting chaos and random fractal theory in a unified manner.

the book covers: DNA sequence analysis EEG analysis Heart. 1. Introduction. Numerous techniques have been developed to measure the complexity of time series generated from nonlinear dynamical systems.

Pincus proposed a family of statistics, called approximate entropy (ApEn), to measure the regularity of a time general, a lower value of ApEn reflects a higher degree of regularity, whereas a less predictable time series is often Cited by: Chaotic Time Series Analysis. Jianbo Gao. Department of Electrical and Computer Engineering, University of Florida Book Author(s): Jianbo Gao.

Multiscale Analysis of Complex Time Series: Integration of Chaos and Random Fractal Theory, and Beyond. Related. Multiscale Analysis of Complex Time Series fills this pressing need by presenting chaos and random fractal theory in a unified manner.

Adopting a data-driven approach, the book covers: DNA sequence analysis EEG analysis Heart rate variability analysis Neural information processing Network traffic modeling Economic time series analysis And more.

Multiscale Entropy Analysis of Complex Physiologic Time Series Article (PDF Available) in Physical Review Letters 89(6) September with 1, Reads How we measure 'reads'.

After spending much time in looking for a good book about multiscale analysis of time series, I found this wonderful book by Jianbo Gao and his colleagues. The book summarizes the current approaches to investigate complex time series analysis, with many applications to real world data.

Many (old and new) theories and many (old and new) methods 5/5(5). For scale (τ = 1), the time series y 1 is the original time series. The length of each time series is equal N ∕ τ, where N is the length of the original time series. We can calculate the Tsallis permutation entropy for each time series y τ.

multiscale Tsallis permutation entropy is defined as follows: (7) S q, τ = 1 − ∑ i = 1 W p i. Different time series analysis methods have been developed to fulfill this challenging task, e.g., chaos analysis, fractal analysis [2,3], recurrence plot, complexity measure, multiscale entropy, and time-frequency representation.

Time series analysis has been broadly adopted in scientific research and engineering applications. Multiscale Analysis of Complex Time Series fills this pressing need by presenting chaos and random fractal theory in a unified manner.

Adopting a data-driven approach, the book covers: DNA sequence analysis - EEG analysis - Heart rate variability analysis - Neural information processing - Network traffic modeling - Economic time series analysis. Multiscale Signal Analysis and Modeling is an ideal book for graduate students and practitioners, especially those working in or studying the field of signal/image processing, telecommunication and applied statistics.

It can also serve as a reference book for engineers, researchers and educators interested in mathematical and statistical modeling. Measuring complexity of observed time series plays an important role for understanding the characteristics of the system under study.

Permutation entropy (PE) is a powerful tool for complexity analysis, but it has some limitations. For example, the amplitude information is discarded; the equalities (i.e., equal values in the analysed signal) are not properly dealt with; and the performance.

Multiscale Analysis of Complex Time Series fills this pressing need by presenting chaos and random fractal theory in a unified manner. Adopting a data-driven approach, the book covers: DNA sequence analysisPrice: $   In this letter, focusing on the above challenge, we propose a reliable method for constructing a multiscale complex network from multivariate time series, inspired by the multivariate multiscale entropy analysis [50–52], aiming to encode the underlying local dynamic behavior of multivariate time series into the network topology.

Multiscale entropy analysis of complex physiologic time series. @article{CostaMultiscaleEA, title={Multiscale entropy analysis of complex physiologic time series.}, author={Madalena Costa and Ary L.

Goldberger and C K Peng}, journal={Physical review letters}, year={}, volume={89 6}. Get this from a library. Multiscale analysis of complex time series: integration of chaos and random fractal theory, and beyond.

[Jianbo Gao;] -- "Chaos and random fractal theory are two of the most important theories developed for data analysis. Until now, there has been no single book that encompasses all of the basic concepts necessary for. Jianbo Gao is the author of Multiscale Analysis of Complex Time Series ( avg rating, 0 ratings, 0 reviews, published ) and Fractal Analyses ( a.

Summary This chapter contains sections titled: Examples of Multiscale Phenomena Examples of Challenging Problems to be Pursued Outline of the Book Bibliographic Notes Introduction - Multiscale Analysis of Complex Time Series - Wiley Online Library.

This paper presents a novel framework to determine the number of resolution levels in the application of a wavelet transformation to a rainfall time series. The rainfall time series are decomposed using the à trous wavelet transform.

Then, multi-scale entropy (MSE) analysis that helps to elucidate some hidden characteristics of the original rainfall time series is applied to the decomposed. Multiscale entropy (MSE) is an effective algorithm for measuring the complexity of a time series that has been applied in many fields successfully.

However, MSE may yield an inaccurate estimation of entropy or induce undefined entropy because the coarse-graining procedure reduces the length of a time series considerably at large by: Multiscale Analysis of Complex Time Series fills this pressing need by presenting chaos and random fractal theory in a unified manner.

Adopting a data-driven approach, the book covers: DNA sequence analysis. Request PDF | Analysis of complex time series using refined composite multiscale entropy | Multiscale entropy (MSE) is an effective algorithm for measuring the complexity of a time series.

Multiscale entropy (MSE) has been widely and successfully used in analyzing the complexity of physiological time series. We reinterpret the averaging process in MSE as filtering a time series by a.

The results support a general "complexity-loss" theory of aging and disease. We also apply the method to the analysis of coding and noncoding DNA sequences and find that the latter have higher multiscale entropy, consistent with the emerging view that so-called "junk DNA" sequences contain important biological information.

Analysis of complex time series using refined composite multiscale entropy. Book Author(s): Jianbo Gao. Department of Electrical and Computer Engineering, University of Florida. Search for more papers by this author Multiscale Analysis of Complex Time Series: Integration of Chaos and Random Fractal Theory, and Beyond.

Related; Information; Close Figure Viewer. Browse All Figures Return to Figure. "Chaos and random fractal theory are two of the most important theories developed for data analysis. Until now, there has been no single book that encompasses all of the basic concepts necessary for researchers to fully understand the ever-expanding literature and apply novel methods to effectively solve their signal processing problems.

Multiscale Analysis of Complex Time Series fills this. Multiscale Entropy Analysis of Complex Physiologic Time Series Madalena Costa,1,2 Ary L. Goldberger,1 and C.-K. Peng1 1Cardiovascular Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts 2Institute of Biophysics and Biomedical Engineering, Faculty of Science of the University of Lisbon, Campo Grande, Lisbon, Portugal.

We introduce a generalization of multiscale entropy (MSE) analysis. The method is termed MSE n, where the subscript denotes the moment used to coarse-grain a time μ, described previously, uses the mean value (first moment).Here, we focus on MSE σ 2, which uses the second moment, i.e., the variance.

MSE σ 2 quantifies the dynamics of the volatility (variance) of a signal over. Examples of Time Series 1 Objectives of Time Series Analysis 6 Some Simple Time Series Models 7 Some Zero-Mean Models 8 Models with Trend and Seasonality 9 A General Approach to Time Series Modeling 14 Stationary Models and the Autocorrelation Function 15 The Sample Autocorrelation Function 18 There has been considerable interest in quantifying the complexity of physiologic time series, such as heart rate.

However, traditional algorithms indicate higher complexity for certain pathologic processes associated with random outputs than for healthy dynamics exhibiting long-range correlations.

This paradox may be due to the fact that conventional algorithms fail to account for the. Our method enables to map a multivariate time series into a multiscale complex network, which allows us to investigate the inherent properties of multivariate time series from the perspective of complex network analysis and multiscale analysis.

Then we use network clustering measure to characterize the inherent structure of the MMCN. The multiscale phenomenon widely exists in nonlinear complex systems.

One efficient way to characterize complex systems is to measure time series and then extract information from the measurements. We propose a reliable method for constructing a multiscale complex network from multivariate time series.

Scaling for the MSE of fractal time series. Among the most widely used models for biological signals, including HRV, EEG, and posture (Gao et al., a), is the fractal time series with long memory, the so-called 1/f α, or 1/f 2H − 1, α = 2H − 1 processes, where 0.

For the complexity analysis of multivariate time series, the multivariate multiscale permutation entropy (MMPE), was proposed, which can incorporate the simultaneous analysis of multi-channel data as a unique block within a multiscale framework.

However, MMPE is built based on MPE without the improvement introduced in IMPE, i.e. the multiple. : Multiscale Modeling and Analysis for Materials Simulation (Lecture Notes Series, Institute for Mathematical Sciences, National University of Singapore) (): Bao, Weizhu, Du, Qiang: BooksFormat: Hardcover.

The multiscale phenomenon widely exists in nonlinear complex systems. One efficient way to characterize complex systems is to measure time series and then extract information from the .CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Quantifying the complexity of physiologic time series has been of considerable interest.

Several entropybased measures have been proposed, although there is no straightforward correspondence between entropy and complexity. These traditional algorithms may generate misleading results because an increase in .Multiscale analysis of economic time series by scale-dependent Lyapunov exponent Jianbo Gao a, Jing Hu b, Wen-Wen Tung c & Yi Zheng d a PMB Intelligence LLC, PO BoxWest Lafayette, INUSA b Affymetrix, Inc., Central Expressway, Santa Clara, CAUSA c Department of Earth & Atmospheric Sciences, Purdue University, West.