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

- 152 Want to read
- 4 Currently reading

Published
**September 17, 2007**
by Wiley-Interscience
.

Written in English

The Physical Object | |
---|---|

Number of Pages | 352 |

ID Numbers | |

Open Library | OL7619718M |

ISBN 10 | 0471654701 |

ISBN 10 | 9780471654704 |

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.

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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.