Multidimensional scaling method for complex time series feature classification based on generalized complexity-invariant distance

2018 ◽  
Vol 95 (4) ◽  
pp. 2875-2892 ◽  
Author(s):  
Du Shang ◽  
Pengjian Shang ◽  
Liu Liu
Keyword(s):  
Time Series ◽  
Multidimensional Scaling ◽  
Feature Classification ◽  
Scaling Method ◽  
Complex Time ◽  
Invariant Distance

scholarly journals Scale-dependent intrinsic entropies of complex time series

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150204 ◽  
Author(s):  
Jia-Rong Yeh ◽  
Chung-Kang Peng ◽  
Norden E. Huang
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Spatial Scales ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Complex Time ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Simulated Time ◽  
Simulated Time Series

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal’s complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease.

scholarly journals Disentangling the stochastic behavior of complex time series

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Mehrnaz Anvari ◽  
M. Reza Rahimi Tabar ◽  
Joachim Peinke ◽  
Klaus Lehnertz
Keyword(s):  
Time Series ◽  
Stochastic Behavior ◽  
Complex Time

scholarly journals Mapping Perception of Consumer Antivirus Software with Multidimensional Scaling Method

2019 ◽  
Vol 4 (3) ◽  
pp. 91-95
Author(s):  
Ai Nurhayati ◽  
Frencius Frencius
Keyword(s):  
Multidimensional Scaling ◽  
Software Industry ◽  
Three Dimensional ◽  
Global Scale ◽  
Scaling Method ◽  
Know How ◽  
Antivirus Software ◽  
The World ◽  
Spss Software ◽  
Program Version

Antivirus software industry is growing rapidly in the world in 2018. The domestic antivirus software industry must be able to compete on a global scale. To face free trade, Indonesia's antivirus software industry must be able to know its position in the minds of consumers, especially domestic consumers. In this research, Smadav will represent the antivirus software industry from Indonesia.In this research want to know how the position of smadav compared with its current competitors, namely Avast, Avira, AVG, Kaspersky, McAfee and Norton. This research is only done to map antivirus software based on similarity according to respondent's perception.This research uses Multidimensional scaling (MDS) method through SPSS software program version 23. The results showed that there are three groups of different antivirus software based on similarity level according to the respondent's perception.On the two-dimensional and three-dimensional maps Norton antivirus software, Avast and Avira have similar resemblance according to the respondent's perception, because the location is closest and is in the same quadrant. Smadav differs according to perceptions of respondents. AVG, McAfee and Kaspersky have similarities according to respondents' perceptions.

Ranking of Fuzzy Numbers by using Scaling Method

2019 ◽  
Vol 3 (2) ◽  
pp. 137-143
Author(s):  
Ayad Mohammed Ramadan
Keyword(s):  
Multidimensional Scaling ◽  
Fuzzy Number ◽  
Fuzzy Numbers ◽  
Triangular Fuzzy Numbers ◽  
Scaling Method ◽  
Ranking Of Fuzzy Numbers ◽  
First Time

In this paper, we presented for the first time a multidimensional scaling approach to find the scaling as well as the ranking of triangular fuzzy numbers. Each fuzzy number was represented by a row in a matrix, and then found the configuration points (scale points) which represent the fuzzy numbers in . Since these points are not uniquely determined, then we presented different techniques to reconfigure the points to compare them with other methods. The results showed the ability of ranking fuzzy numbers

Analysis of Time Series Based on a New Entropy Plane by Using Weighted Dispersion Pattern

2021 ◽  
Vol 31 (09) ◽  
pp. 2150128
Author(s):  
Guyue Qin ◽  
Pengjian Shang
Keyword(s):  
Time Series ◽  
Financial Markets ◽  
Stock Markets ◽  
Simulated Data ◽  
Tsallis Entropy ◽  
Dispersion Pattern ◽  
Complex Time ◽  
Plane Method ◽  
Stock Indexes ◽  
Analysis Of Time Series

Complexity is an important feature of complex time series. In this paper, we construct a weighted dispersion pattern and propose a new entropy plane using past Tsallis entropy and past Rényi entropy by using weighted dispersion pattern (PTEWD and PREWD, respectively), to quantify the complexity of time series. Through analyzing simulated data and actual data, we have verified the reliability of the entropy plane method. This entropy plane successfully distinguishes American and Chinese stock indexes, as well as developed and emergent stock markets. We introduce PTEWD and PREWD into multiscale settings, which could also well distinguish different stock markets. The results show that the new entropy plane could be used as an effective tool to distinguish financial markets.

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