Complex Similarity and Fluctuation Dynamics of Financial Markets on Voter Interacting Dynamic System

2018 ◽  
Vol 28 (13) ◽  
pp. 1850156 ◽  
Author(s):  
Rui Li ◽  
Jun Wang ◽  
Guochao Wang
Keyword(s):  
Financial Markets ◽  
Cross Correlation ◽  
Simulated Data ◽  
Intrinsic Mode Functions ◽  
Dynamical Behaviors ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Fluctuation Dynamics ◽  
Random Property

A financial price dynamics is developed based on the voter interacting system, in an attempt to investigate and reproduce the complex similarity and the fluctuation dynamics of financial markets. The complexity-invariance distance (CID) is applied to study the similarity of each stock pairs. A simple classification of seven real indexes and the simulated data is obtained according to the CID values for each stock pairs. The corresponding multiscale dynamical behaviors of CID values are also studied by combining CID with the multiscale method. Further, the similarity of the newest data and the historical data of the returns is investigated by a novel auto-CID analysis, and a corresponding exponent relationship is exhibited. Moveover, the cross correlation function (CCF) is applied to study the correlation of each stock pairs and the causalities of these stock pairs are investigated by the Granger causality method. Besides, the complexity and the randomness of fluctuations of returns, surrogate returns, shuffled returns and intrinsic mode functions (derived from empirical mode decomposition) are also explored at different thresholds with Lempel–Ziv complexity. The empirical study shows complex similarity and similar random property between the proposed price model and the real stock markets, which exhibits that the proposed model is feasible to some extent.

Analysis of statistical coefficients and autoregressive parameters over intrinsic mode functions (IMFs) for epileptic seizure detection

2020 ◽  
Vol 65 (6) ◽  
pp. 693-704
Author(s):  
Rafik Djemili
Keyword(s):  
Involuntary Movement ◽  
Epileptic Seizures ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Time Segment ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Finite Set ◽  
Student’S T

AbstractEpilepsy is a persistent neurological disorder impacting over 50 million people around the world. It is characterized by repeated seizures defined as brief episodes of involuntary movement that might entail the human body. Electroencephalography (EEG) signals are usually used for the detection of epileptic seizures. This paper introduces a new feature extraction method for the classification of seizure and seizure-free EEG time segments. The proposed method relies on the empirical mode decomposition (EMD), statistics and autoregressive (AR) parameters. The EMD method decomposes an EEG time segment into a finite set of intrinsic mode functions (IMFs) from which statistical coefficients and autoregressive parameters are computed. Nevertheless, the calculated features could be of high dimension as the number of IMFs increases, the Student’s t-test and the Mann–Whitney U test were thus employed for features ranking in order to withdraw lower significant features. The obtained features have been used for the classification of seizure and seizure-free EEG signals by the application of a feed-forward multilayer perceptron neural network (MLPNN) classifier. Experimental results carried out on the EEG database provided by the University of Bonn, Germany, demonstrated the effectiveness of the proposed method which performance assessed by the classification accuracy (CA) is compared to other existing performances reported in the literature.

scholarly journals Gear Crack Level Classification Based on EMD and EDT

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Haiping Li ◽  
Jianmin Zhao ◽  
Xinghui Zhang ◽  
Hongzhi Teng
Keyword(s):  
Vibration Signal ◽  
Test Sample ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Euclidean Distances ◽  
Accuracy Rates ◽  
Gear Crack ◽  
Fault Information

Gears are the most essential parts in rotating machinery. Crack fault is one of damage modes most frequently occurring in gears. So, this paper deals with the problem of different crack levels classification. The proposed method is mainly based on empirical mode decomposition (EMD) and Euclidean distance technique (EDT). First, vibration signal acquired by accelerometer is processed by EMD and intrinsic mode functions (IMFs) are obtained. Then, a correlation coefficient based method is proposed to select the sensitive IMFs which contain main gear fault information. And energy of these IMFs is chosen as the fault feature by comparing with kurtosis and skewness. Finally, Euclidean distances between test sample and four classes trained samples are calculated, and on this basis, fault level classification of the test sample can be made. The proposed approach is tested and validated through a gearbox experiment, in which four crack levels and three kinds of loads are utilized. The results show that the proposed method has high accuracy rates in classifying different crack levels and may be adaptive to different conditions.

scholarly journals ELM and K-nn machine learning in classification of breath sounds signals

2020 ◽  
Vol 10 (4) ◽  
pp. 3528
Author(s):  
Z. Neili ◽  
M. Fezari ◽  
A. Redjati
Keyword(s):  
Machine Learning ◽  
Binary Classification ◽  
Machine Learning Algorithms ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Electronic Stethoscope ◽  
Mode Decomposition ◽  
Breath Sounds ◽  
Learning Machine

The acquisition of Breath sounds (BS) signals from a human respiratory system with an electronic stethoscope, provide and offer prominent information which helps the doctors to diagnosis and classification of pulmonary diseases. Unfortunately, this BS signals with other biological signals have a non-stationary nature according to the variation of the lung volume, and this nature makes it difficult to analyze and classify between several diseases. In this study, we were focused on comparing the ability of the extreme learning machine (ELM) and k-nearest neighbour (K-nn) machine learning algorithms in the classification of adventitious and normal breath sounds. To do so, the empirical mode decomposition (EMD) was used in this work to analyze BS, this method is rarely used in the breath sounds analysis. After the EMD decomposition of the signals into Intrinsic Mode Functions (IMFs), the Hjorth descriptors (Activity) and Permutation Entropy (PE) features were extracted from each IMFs and combined for classification stage. The study has found that the combination of features (activity and PE) yielded an accuracy of 90.71%, 95% using ELM and K-nn respectively in binary classification (normal and abnormal breath sounds), and 83.57%, 86.42% in multiclass classification (five classes).

Geomagnetic signal de-noising method based on improved empirical mode decomposition and morphological filtering

2020 ◽  
pp. 095441002095102
Author(s):  
Hongqi Zhai ◽  
Lihui Wang ◽  
Qingya Liu ◽  
Nan Qiao
Keyword(s):  
Empirical Mode Decomposition ◽  
Cross Correlation ◽  
Random Noise ◽  
Pulse Signal ◽  
Structural Elements ◽  
Intrinsic Mode Functions ◽  
Pulse Interference ◽  
Mode Decomposition ◽  
Morphological Filtering ◽  
Mean Square Errors

To solve the problem that geomagnetic signals are susceptible to random noise and instantaneous pulse interference in geomagnetic navigation, a geomagnetic signal de-noising method based on improved empirical mode decomposition (IEMD) and morphological filtering (MF) is proposed. The instantaneous pulse interference is eliminated by designing different structural elements according to the characteristics of the pulse signal. The signal after filtering the instantaneous pulse interference is decomposed by EMD, and the intrinsic mode functions (IMFs) obtained from the decomposition are determined as two modes (i.e. noise IMFs and mixed IMFs) by the cross-correlation coefficient criterion. The noise IMFs are removed directly, and a normalized least means square filter (NLMS) is designed to remove noise from mixed IMFs, which can adaptively adjust the filtering parameters according to the noise level of different IMF components. The noise-reduced mixed IMFs and residual are reconstructed to obtain the final geomagnetic signal. Experiment results illustrate that the proposed MF-IEMD method can effectively achieve noise reduction. Comparing with the traditional EMD and MF-EMD de-noising methods, the root mean square errors(RMSE) decreased by 49.27% and 24.79%, respectively.

Automated Classification of Focal and Non-Focal EEG Signals Based on Bivariate Empirical Mode Decomposition

2018 ◽  
pp. 13-33 ◽  
Author(s):  
Rajeev Sharma ◽  
Ram Bilas Pachori
Keyword(s):  
Empirical Mode Decomposition ◽  
Support Vector ◽  
Automated Classification ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Mode Decomposition ◽  
Electroencephalogram Eeg ◽  
Aided Diagnosis ◽  
Bivariate Empirical Mode Decomposition

The chapter presents a new approach of computer aided diagnosis of focal electroencephalogram (EEG) signals by applying bivariate empirical mode decomposition (BEMD). Firstly, the focal and non-focal EEG signals are decomposed using the BEMD, which results in intrinsic mode functions (IMFs) corresponding to each signal. Secondly, bivariate bandwidths namely, amplitude bandwidth, precession bandwidth, and deformation bandwidth are computed for each obtained IMF. Interquartile range (IQR) values of bivariate bandwidths of IMFs are employed as the features for classification. In order to perform classification least squares support vector machine (LS-SVM) is used. The results of the experiment suggest that the computed bivariate bandwidths are significantly useful to discriminate focal EEG signals. The resultant classification accuracy obtained using proposed methodology, applied on the Bern-Barcelona EEG database, is 84.01%. The obtained results are encouraging and the proposed methodology can be helpful for identification of epileptogenic focus.

scholarly journals EMD Method for Minimizing the Effect of Seasonal Trends in Detrended Cross-Correlation Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Keqiang Dong ◽  
You Gao ◽  
Nianpeng Wang
Keyword(s):  
Correlation Analysis ◽  
Long Range ◽  
Cross Correlation ◽  
Intrinsic Mode Functions ◽  
Nonstationary Signals ◽  
Seasonal Trends ◽  
Mode Decomposition ◽  
Cross Correlation Analysis ◽  
Correlated Signals ◽  
Emd Method

Detrended cross-correlation analysis (DCCA) is a scaling method commonly used to estimate long-range power-law cross-correlation in nonstationary signals. Recent studies have reported signals superimposed with trends, which often lead to the complexity of the signals and the susceptibility of DCCA. This paper artificially generates long-range cross-correlated signals and systematically investigates the effect of seasonal trends. Specifically, for the crossovers raised by trends, we propose a smoothing algorithm based on empirical mode decomposition (EMD) method which decomposes underlying signals into several intrinsic mode functions (IMFs) and a residual trend. After the removal of slowly oscillating components and residual term, seasonal trends are eliminated.

scholarly journals Classification of Focal and Non-Focal EEG Signal using an Area of Octagon Method

2019 ◽  
Vol 9 (1) ◽  
pp. 1832-1838
Keyword(s):  
Therapy Resistance ◽  
Eeg Signal ◽  
Intrinsic Mode Functions ◽  
Average Classification Accuracy ◽  
Input Feature ◽  
Linear Polynomial ◽  
Mode Decomposition ◽  
Rbf Kernel ◽  
Electroencephalogram Eeg

Epilepsy, a neurological syndrome can be detected via the electroencephalogram (EEG) signal with the help of sensors placing in the human cranium. This article introduces a fresh method known as the Area of Octagon (AOO), used for Focal (F) and Non-Focal (NF) EEG Signal classification. Initially, both class signals are putrefied into many intrinsic mode functions (IMF) with the help of Empirical mode decomposition (EMD) algorithm. The AOO can be computed with the help of decomposed IMFs. The AOO is now used as an input feature set for the classifier. This research aims to discriminate the F and NF EEG measurements for the therapy resistance. The proposed method attained an average classification accuracy of 97.9% with Linear, polynomial and an RBF kernel.

scholarly journals A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Guohui Li ◽  
Songling Zhang ◽  
Hong Yang
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Extreme Point ◽  
Prediction Models ◽  
Original Data ◽  
Symmetric Mode ◽  
Intrinsic Mode Functions ◽  
Model Based ◽  
Mode Decomposition ◽  
Proposed Model

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed IMFs and residuals are the final prediction results. Six kinds of prediction models are compared, which are DBN prediction model, EMD-DBN prediction model, EEMD-DBN prediction model, CEEMD-DBN prediction model, ESMD-DBN prediction model, and the proposed model in this paper. The same sunspots time series are predicted with six kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error.

CLASSIFICATION OF ARRHYTHMIA IN ELECTROCARDIOGRAM USING EMD BASED FEATURES AND SUPPORT VECTOR MACHINE WITH MARGIN SAMPLING

2013 ◽  
Vol 12 (03) ◽  
pp. 1350015 ◽  
Author(s):  
M. SUCHETHA ◽  
N. KUMARAVEL
Keyword(s):  
Support Vector Machine ◽  
Information Source ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Mode Decomposition ◽  
Computer Based ◽  
Value Decomposition

Electrocardiogram (ECG) signals represent a useful information source about the rhythm and the functioning of the heart. Any disturbance in the heart's normal rhythmic contraction is called an arrhythmia. Analysis of Electrocardiogram signals is the most effective available method for diagnosing cardiac arrhythmias. Computer based classification of ECG provides higher accuracy and offer a potential of an affordable cardiac abnormality mass screening. The empirical mode decomposition is performed on various arrhythmia signals and different levels of intrinsic mode functions (IMF) are obtained. Singular value decomposition (SVD) is used to extract features from the IMF and classification is performed using support vector machine. This method is more efficient for classification of ECG signals and at the same time provides good generalization properties.

AN IMPROVED ONLINE PARADIGM FOR SCREENING OF DIABETIC PATIENTS USING RR-INTERVAL SIGNALS

2016 ◽  
Vol 16 (01) ◽  
pp. 1640003 ◽  
Author(s):  
RAM BILAS PACHORI ◽  
MOHIT KUMAR ◽  
PAKALA AVINASH ◽  
KORA SHASHANK ◽  
U. RAJENDRA ACHARYA
Keyword(s):  
Kernel Function ◽  
Morlet Wavelet ◽  
Least Square ◽  
Support Vector ◽  
Svm Classifier ◽  
Diabetic Patients ◽  
Intrinsic Mode Functions ◽  
Rr Interval ◽  
Mode Decomposition

Diabetes Mellitus (DM) which is a chronic disease and difficult to cure. If diabetes is not treated in a timely manner, it may cause serious complications. For timely treatment, an early detection of the disease is of great interest. Diabetes can be detected by analyzing the RR-interval signals. This work presents a methodology for classification of diabetic and normal RR-interval signals. Firstly, empirical mode decomposition (EMD) method is applied to decompose the RR-interval signals in to intrinsic mode functions (IMFs). Then five parameters namely, area of analytic signal representation (AASR), mean frequency computed using Fourier-Bessel series expansion (MFFB), area of ellipse evaluated from second-order difference plot (ASODP), bandwidth due to frequency modulation (BFM) and bandwidth due to amplitude modulation (BAM) are extracted from IMFs obtained from RR-interval signals. Statistically significant features are fed to least square-support vector machine (LS-SVM) classifier. The three kernels namely, Radial Basis Function (RBF), Morlet wavelet, and Mexican hat wavelet kernels have been studied to obtain the suitable kernel function for the classification of diabetic and normal RR-interval signals. In this work, we have obtained the highest classification accuracy of 95.63%, using Morlet wavelet kernel function with 10-fold cross-validation. The classification system proposed in this work can help the clinicians to diagnose diabetes using electrocardiogram (ECG) signals.

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