Time series based behavior pattern quantification analysis and prediction — A study on animal behavior

2020 ◽  
Vol 540 ◽  
pp. 122884
Author(s):  
Wuhao Jiang ◽  
Kai Wang ◽  
Yan Lv ◽  
Jianfeng Guo ◽  
Zhongjin Ni ◽  
...  
Keyword(s):  
Time Series ◽  
Animal Behavior ◽  
Behavior Pattern ◽  
Quantification Analysis ◽  
Pattern Quantification

Influences of Embedding Parameters and Segment Sizes in Recursive Characteristics Analysis on Coefficients of Friction

2021 ◽  
Vol 31 (04) ◽  
pp. 2150058
Author(s):  
Guodong Sun ◽  
Chao Zhang ◽  
Hua Zhu ◽  
Shihui Lang
Keyword(s):  
Time Series ◽  
Dynamical Evolution ◽  
Chaotic Time Series ◽  
Computational Time ◽  
Data Sets ◽  
Self Similarity ◽  
Tribological Behaviors ◽  
Characteristics Analysis ◽  
Quantification Analysis ◽  
Recursive Matrix

The methods of recurrence plots (RPs) and recurrence quantification analysis (RQA) have been used to investigate the tribosystem. The morphology of RPs and RQA measures are strongly dependent on the embedding parameters of the recursive matrix and the segment sizes of the time-series. To improve the calculation accuracy of recursive characteristics analysis, the influences of the embedding parameters and segment sizes on the morphology of RPs and RQA measures have been studied in this letter. Three kinds of theoretical chaotic time-series and measured coefficient of friction (COF) signals during the running-in process were chosen as research objects, and the morphology of RPs and RQA measures were obtained using CRP toolbox afterward. The results indicate that no embedding was actually needed if the data sets are to be qualitatively analyzed using RPs and RQA. Additionally, the morphology of RPs and RQA measures are sensitive to the segment sizes for theoretical chaotic time-series, while the RQA measures of COF signal in the steady-state period are rather stable due to its self-similarity. Finally, according to the guidelines of the parameter settings, the dynamical evolution of measured COF signals during the running-in process have been investigated. It is indicated that recursive characteristics of COF signals could reveal the tribological behaviors’ evolution and conduct the running-in status identification. The results in this paper are significant for improving the calculation accuracy and saving computational time when using the method of recursive characteristics analysis on the tribological behaviors.

Recurrence Quantification Analysis of Financial Markets

2013 ◽  
pp. 50-62
Author(s):  
João A. Bastos
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Recurrence Quantification Analysis ◽  
Government Bonds ◽  
Financial Time ◽  
Analysis Technique ◽  
Recurrence Quantification ◽  
Us Government ◽  
Minimum Number ◽  
Quantification Analysis

Recurrence quantification analysis is a nonlinear time series analysis technique that detects deterministic dependencies in time series. This technique is particularly appropriate for modeling financial time series since it requires no assumptions on stationarity, statistical distribution, and minimum number of observations. This chapter illustrates two applications of recurrence quantification analysis to financial data: a set of international stock indices, and zero-coupon yields of US government bonds.

Dynamics of Pledge Behavior of Crowdfunded Projects

2017 ◽  
Vol 8 (1) ◽  
pp. 72-86 ◽  
Author(s):  
Jaya Gera ◽  
Harmeet Kaur
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Euclidean Distance ◽  
Project Success ◽  
Behavior Pattern ◽  
Key Indicator ◽  
Overall Performance

This paper aims to provide ways to enhance overall performance of crowdfunding platforms by improving success prospects of projects post-launch. Pledge behavior at the initial stages of project launch is a key indicator of project success. So, this work identifies projects to be promoted on basis of their pledge behavior at such a crucial phase. The time series of pledge amount is analyzed to understand dynamics of funding pattern and to predict a project's chances of successful funding. Statistical analysis was performed on two different datasets of projects launched over crowdfunding platform Kickstarter. The results obtained provide better understanding of the funding pattern of successful and unsuccessful projects. On the basis of behavior pattern, projects are classified as overfunded, funded, potential and low potential. To classify a project, Euclidean distance of the target project with median of the funding pattern of different categories is used to find closest category to which a project belongs. This process is effective and less expensive in terms of computation.

scholarly journals Frequency spectrum recurrence analysis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Guênia Ladeira ◽  
Norbert Marwan ◽  
João-Batista Destro-Filho ◽  
Camila Davi Ramos ◽  
Gabriela Lima
Keyword(s):  
Time Series ◽  
Frequency Spectrum ◽  
Concentration Level ◽  
Recurrence Analysis ◽  
Similar Frequency ◽  
Eyes Closed ◽  
Analysis Technique ◽  
Recurrence Quantification ◽  
Eyes Open ◽  
Quantification Analysis

AbstractIn this paper, we present the new frequency spectrum recurrence analysis technique by means of electro-encephalon signals (EES) analyses. The technique is suitable for time series analysis with noise and disturbances. EES were collected, and alpha waves of the occipital region were analysed by comparing the signals from participants in two states, eyes open and eyes closed. Firstly, EES were characterized and analysed by means of techniques already known to compare with the results of the innovative technique that we present here. We verified that, standard recurrence quantification analysis by means of EES time series cannot statistically distinguish the two states. However, the new frequency spectrum recurrence quantification exhibit quantitatively whether the participants have their eyes open or closed. In sequence, new quantifiers are created for analysing the recurrence concentration on frequency bands. These analyses show that EES with similar frequency spectrum have different recurrence levels revealing different behaviours of the nervous system. The technique can be used to deepen the study on depression, stress, concentration level and other neurological issues and also can be used in any complex system.

RECURRENCE QUANTIFICATION ANALYSIS OF TEMPERATURE FLUCTUATIONS IN A HORIZONTAL ROUND HEATED TURBULENT JET

2009 ◽  
Vol 19 (08) ◽  
pp. 2487-2498 ◽  
Author(s):  
T. E. KARAKASIDIS ◽  
A. LIAKOPOULOS ◽  
A. FRAGKOU ◽  
P. PAPANICOLAOU
Keyword(s):  
Time Series ◽  
Temperature Fluctuations ◽  
Recurrence Quantification Analysis ◽  
Turbulent Jet ◽  
Transitional Flow ◽  
Horizontal Line ◽  
Recurrence Quantification ◽  
Quantification Analysis ◽  
Instantaneous Temperature ◽  
Temperature Time

We present an analysis of temperature fluctuations in a horizontal round heated turbulent jet. Instantaneous temperature time series were recorded at several points along a horizontal line in the plane of symmetry of the jet. The time series are analyzed using Recurrence Quantification Analysis (RQA). The variation of characteristic RQA measures is associated with and interpreted via the transitions of the physical state of the fluid from the fully-turbulent flow near the jet centerline to the transitional flow near the boundary of the jet.

scholarly journals Detection of Two-Phase Flow Patterns in a Vertical Minichannel Using the Recurrence Quantification Analysis

2015 ◽  
Vol 9 (2) ◽  
pp. 99-104
Author(s):  
Romuald Mosdorf ◽  
Grzegorz Górski
Keyword(s):  
Time Series ◽  
Two Phase Flow ◽  
Bubbly Flow ◽  
Flow Patterns ◽  
Recurrence Quantification Analysis ◽  
Phase Flow ◽  
Two Phase ◽  
Signal Characteristics ◽  
Recurrence Quantification ◽  
Quantification Analysis

Abstract The two-phase flow (water-air) occurring in square minichannel (3x3 mm) has been analysed. In the minichannel it has been observed: bubbly flow, flow of confined bubbles, flow of elongated bubbles, slug flow and semi-annular flow. The time series recorded by laser-phototransistor sensor was analysed using the recurrence quantification analysis. The two coefficients:Recurrence rate (RR) and Determinism (DET) have been used for identification of differences between the dynamics of two-phase flow patterns. The algorithm which has been used normalizes the analysed time series before calculating the recurrence plots.Therefore in analysis the quantitative signal characteristicswas neglected. Despite of the neglect of quantitative signal characteristics the analysis of its dynamics (chart of DET vs. RR) allows to identify the two-phase flow patterns. This confirms that this type of analysis can be used to identify the two-phase flow patterns in minichannels.

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