Sub-Sequence Incidence Analysis within Series of Bernoulli Trials: Application in Characterization of Time Series Dynamics

2017 ◽  
Author(s):  
Richard H.G. Jackson
Keyword(s):  
Time Series ◽  
Bernoulli Trials ◽  
Incidence Analysis

Characterization of Irreversible Land Subsidence in the Yazd‐Ardakan Plain, Iran from 2003‐2020 InSAR Time Series

2021 ◽  
Author(s):  
Sayyed Mohammad Javad Mirzadeh ◽  
Shuanggen Jin ◽  
Esmaeel Parizi ◽  
Estelle Chaussard ◽  
Roland Bürgmann ◽  
...  
Keyword(s):  
Time Series ◽  
Land Subsidence

Characterization of Epileptic EEG Time Series (I): Gabor Transform and Nonlinear Dynamics Methods

1997 ◽  
pp. 179-226 ◽  
Author(s):  
Susana Blanco ◽  
Silvia Kochen ◽  
Rodrigo Quian Quiroga ◽  
Luis Riquelme ◽  
Osvaldo A. Rosso ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Gabor Transform

Characterization of Time Series Data

2017 ◽  
pp. 211-230
Author(s):  
W. Kulp Christopher ◽  
J. Niskala Brandon
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data

Representative Time Series Fractal Analysis of the Traffic Flows of a Dedicated High-Speed Link

2021 ◽  
Author(s):  
Ginno Millan ◽  
manuel vargas ◽  
Guillermo Fuertes
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Long Range ◽  
Computer Networks ◽  
Fractal Analysis ◽  
High Speed ◽  
Long Range Dependence ◽  
Traffic Flows ◽  
Speed Computer

Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link exhibited fractal behavior since the Hurst exponent was in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between -0.5, 0. Based on these results, it is ideal to characterize both the singularities of the fractal traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyzes, the fact that the traffic flows of current computer networks exhibited fractal behavior with a long-range dependence was reaffirmed.

Soft Computing Based Statistical Time Series Analysis, Characterization of Chaos Theory, and Theory of Fractals

2013 ◽  
pp. 30-61 ◽  
Author(s):  
Mofazzal H. Khondekar ◽  
Dipendra N. Ghosh ◽  
Koushik Ghosh ◽  
Anup Kumar Bhattacharya
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Time Series Analysis ◽  
Soft Computing ◽  
Chaos Theory ◽  
Filter Design ◽  
Theoretical Perspective ◽  
Series Analysis ◽  
Statistical Time

The present work is an attempt to analyze the various researches already carried out from the theoretical perspective in the field of soft computing based time series analysis, characterization of chaos, and theory of fractals. Emphasis has been given in the analysis on soft computing based study in prediction, data compression, explanatory analysis, signal processing, filter design, tracing chaotic behaviour, and estimation of fractal dimension of time series. The present work is a study as a whole revealing the effectiveness as well as the shortcomings of the various techniques adapted in this regard.

Sign in / Sign up

Export Citation Format

Share Document