scholarly journals Non-Linear Analysis of River System Dynamics Using Recurrence Quantification Analysis

AppliedMath ◽  
2022 ◽  
Vol 2 (1) ◽  
pp. 1-15
Author(s):  
Athanasios Fragkou ◽  
Avraam Charakopoulos ◽  
Theodoros Karakasidis ◽  
Antonios Liakopoulos
Keyword(s):  
Time Series ◽  
Energy Policy ◽  
Linear Time ◽  
Time Windows ◽  
River System ◽  
Recurrence Quantification Analysis ◽  
Environmental Data ◽  
Recurrence Quantification ◽  
Non Linear ◽  
Quantification Analysis

Understanding the underlying processes and extracting detailed characteristics of rivers is critical and has not yet been fully developed. The purpose of this study was to examine the performance of non-linear time series methods on environmental data. Specifically, we performed an analysis of water level measurements, extracted from sensors, located on specified stations along the Nestos River (Greece), with Recurrence Plots (RP) and Recurrence Quantification Analysis (RQA) methods. A more detailed inspection with the sliding windows (epoqs) method was applied on the Recurrence Rate, Average Diagonal Line and Trapping Time parameters, with results showing phase transitions providing useful information about the dynamics of the system. The suggested method seems to be promising for the detection of the dynamical transitions that can characterize distinct time windows of the time series and reveals information about the changes in state within the whole time series. The results will be useful for designing the energy policy investments of producers and also will be helpful for dam management assessment as well as government energy policy.

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.

AUTOMATED IDENTIFICATION OF EPILEPTIC AND ALCOHOLIC EEG SIGNALS USING RECURRENCE QUANTIFICATION ANALYSIS

2012 ◽  
Vol 12 (05) ◽  
pp. 1240028 ◽  
Author(s):  
EE PING NG ◽  
TEIK-CHENG LIM ◽  
SUBHAGATA CHATTOPADHYAY ◽  
MURALIDHAR BAIRY
Keyword(s):  
Nearest Neighbor ◽  
Probabilistic Neural Network ◽  
Gaussian Mixture ◽  
Recurrence Quantification Analysis ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Recurrence Quantification ◽  
Non Linear ◽  
Quantification Analysis

Epilepsy is a common neurological disorder characterized by recurrence seizures. Alcoholism causes organic changes in the brain, resulting in seizure attacks similar to epileptic fits. Hence, it is challenging to differentiate the cause of fits as epileptic or alcoholism, which is important for deciding on the treatment in the neurology ward. The focus of this paper is to automatically differentiate epileptic, normal, and alcoholic electroencephalogram (EEG) signals. As the EEG signals are non-linear and dynamic in nature, it is difficult to tell the subtle changes in these signals with the help of linear techniques or by the naked eye. Therefore, to analyze the normal (control), epileptic, and alcoholic EEG signals, two non-linear methods, such as recurrence plots (RPs) and then recurrence quantification analysis (RQA) are adopted. Approximately 10 RQA parameters have been used to classify the EEG signals into three distinct classes, i.e., normal, epileptic, and alcoholic. Six classifiers, such as support vector machine (SVM), radial basis probabilistic neural network (RBPNN), decision tree (DT), Gaussian mixture model (GMM), k-nearest neighbor (kNN), and fuzzy Sugeno classifiers have been developed to accomplish this task. Results show that the GMM classifier outperformed the other classifiers with a classification sensitivity of 99.6%, specificity of 98.3%, and accuracy of 98.6%.

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.

scholarly journals Do Exchange Rate Series Present General Dependence? Some Results using Recurrence Quantification Analysis

2013 ◽  
Vol 5 (10) ◽  
pp. 678-686
Author(s):  
Teresa Aparicio ◽  
Dulce Saura .
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Recurrence Quantification Analysis ◽  
Underlying Structure ◽  
Recurrence Quantification ◽  
Non Linear ◽  
Quantification Analysis

In this paper, we apply a methodology based on the “Recurrence Quantification Analysis” to four daily exchange rate returns series. Our aim is to discover if they exhibit some kind of underlying structure, and to find an economic explanation for the behavior of exchange rates. Our results show the existence of structure in all series that, in certain cases, can be identified as non-linear deterministic. We also conclude that, in general, the underlying structure tends to disappear in the most recent periods.

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