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

In this paper, we apply a methodology based on the â€œRecurrence Quantification Analysisâ€ 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.

Download Full-text

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

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519412400283 ◽

2012 ◽

Vol 12 (05) ◽

pp. 1240028 ◽

Cited By ~ 9

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%.

Download Full-text

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

AppliedMath ◽

10.3390/appliedmath2010001 ◽

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.

Download Full-text

APPLICATION OF RECURRENCE QUANTIFICATION ANALYSIS FOR THE AUTOMATED IDENTIFICATION OF EPILEPTIC EEG SIGNALS

International Journal of Neural Systems ◽

10.1142/s0129065711002808 ◽

2011 ◽

Vol 21 (03) ◽

pp. 199-211 ◽

Cited By ~ 185

Author(s):

U. RAJENDRA ACHARYA ◽

S. VINITHA SREE ◽

SUBHAGATA CHATTOPADHYAY ◽

WENWEI YU ◽

PENG CHUAN ALVIN ANG

Keyword(s):

Probabilistic Neural Network ◽

Gaussian Mixture ◽

Recurrence Quantification Analysis ◽

Support Vector ◽

Svm Classifier ◽

K Nearest Neighbor ◽

Eeg Signals ◽

Recurrence Quantification ◽

Non Linear ◽

Quantification Analysis

Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are being researched to analyze the EEG signals. In this work, we use the recorded EEG signals in Recurrence Plots (RP), and extract Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes. Recurrence Plot (RP) is a graph that shows all the times at which a state of the dynamical system recurs. Studies have reported significantly different RQA parameters for the three classes. However, more studies are needed to develop classifiers that use these promising features and present good classification accuracy in differentiating the three types of EEG segments. Therefore, in this work, we have used ten RQA parameters to quantify the important features in the EEG signals.These features were fed to seven different classifiers: Support vector machine (SVM), Gaussian Mixture Model (GMM), Fuzzy Sugeno Classifier, K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree (DT), and Radial Basis Probabilistic Neural Network (RBPNN). Our results show that the SVM classifier was able to identify the EEG class with an average efficiency of 95.6%, sensitivity and specificity of 98.9% and 97.8%, respectively.

Download Full-text