River Water Turbidity Forecasting Based on Phase Space Reconstruction and Support Vector Regression

2010 ◽  
Author(s):  
Jun-dong Wang ◽  
Pei-yan Li ◽  
Yong-ming Zhang ◽  
Wei-gui Qi
Keyword(s):  
Phase Space ◽  
Support Vector Regression ◽  
River Water ◽  
Phase Space Reconstruction ◽  
Support Vector ◽  
Water Turbidity

Phase Space Reconstruction of EEG Signals for Classification of ADHD and Control Adults

2019 ◽  
Vol 51 (2) ◽  
pp. 102-113 ◽  
Author(s):  
Simranjit Kaur ◽  
Sukhwinder Singh ◽  
Priti Arun ◽  
Damanjeet Kaur ◽  
Manoj Bajaj
Keyword(s):  
Feature Selection ◽  
Phase Space ◽  
Performance Test ◽  
Phase Space Reconstruction ◽  
Behavioral Disorder ◽  
Support Vector ◽  
Reconstruction Technique ◽  
Eyes Closed ◽  
Eyes Open ◽  
And Control

Attention deficit hyperactivity disorder (ADHD) is a childhood behavioral disorder that can persist into adulthood. Electroencephalography (EEG) plays a significant role in assessing the neurophysiology of ADHD because of its ability to reveal complex brain activity. The present study proposes an EEG-based diagnosis system using the phase space reconstruction technique to classify ADHD and control adults. Electric activity is recorded for 47 ADHD and 50 control adults during the eyes-open, eyes-closed, and Continuous Performance Test (CPT) condition. Various statistical features are extracted from Euclidean distances based on phase space reconstruction of signals. The proposed system is evaluated with 2 feature selection methods (correlation-based feature selection and particle swarm optimization) and 5 machine learning methods (neural dynamic classifier, support vector machine, enhanced probabilistic neural network, k-nearest neighbor, and naive-Bayes classifier). Experimental results showed the highest testing accuracy of 93.3% under the eyes-open, 90% under the eyes-closed, and 100% under the CPT condition. This study focused on the utility of phase space reconstruction of brain signals to discriminate between ADHD and control adults.

Forecasting Exchange Rates

2016 ◽  
pp. 1864-1883
Author(s):  
Ahmed Radhwan ◽  
Mahmoud Kamel ◽  
Mohammed Y. Dahab ◽  
AboulElla Hassanien
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Exchange Rates ◽  
Support Vector Regression ◽  
Chaos Theory ◽  
Financial Time Series ◽  
Optimization Technique ◽  
Support Vector ◽  
Data Sets ◽  
Financial Time

Accurate forecasting for future events constitutes a fascinating challenge for theoretical and for applied researches. Foreign Exchange market (FOREX) is selected in this research to represent an example of financial systems with a complex behavior. Forecasting a financial time series can be a very hard task due to the inherent uncertainty nature of these systems. It seems very difficult to tell whether a series is stochastic or deterministic chaotic or some combination of these states. More generally, the extent to which a non-linear deterministic process retains its properties when corrupted by noise is also unclear. The noise can affect a system in different ways even though the equations of the system remain deterministic. Since a single reliable statistical test for chaoticity is not available, combining multiple tests is a crucial aspect, especially when one is dealing with limited and noisy data sets like in economic and financial time series. In this research, the authors propose an improved model for forecasting exchange rates based on chaos theory that involves phase space reconstruction from the observed time series and the use of support vector regression (SVR) for forecasting.Given the exchange rates of a currency pair as scalar observations, observed time series is first analyzed to verify the existence of underlying nonlinear dynamics governing its evolution over time. Then, the time series is embedded into a higher dimensional phase space using embedding parameters.In the selection process to find the optimal embedding parameters,a novel method based on the Differential Evolution (DE) geneticalgorithm(as a global optimization technique) was applied. The authors have compared forecasting accuracy of the proposed model against the ordinary use of support vector regression. The experimental results demonstrate that the proposed method, which is based on chaos theory and genetic algorithm,is comparable with the existing approaches.

A multiple-parameter approach for short-term traffic flow prediction

2021 ◽  
pp. 2150245
Author(s):  
Xiaoquan Wang ◽  
Wenjun Li ◽  
Chaoying Yin ◽  
Shaoyu Zeng ◽  
Peng Liu
Keyword(s):  
Phase Space ◽  
Support Vector Regression ◽  
Traffic Flow ◽  
Conditional Entropy ◽  
Entropy Method ◽  
Support Vector ◽  
Embedding Dimension ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction

This study proposes a short-term traffic flow prediction approach based on multiple traffic flow basic parameters, in which the chaos theory and support vector regression are utilized. First, a high-dimensional variable space can be obtained according to the traffic flow fundamental function. Then, a maximum conditional entropy method is proposed to determine the embedding dimension. And multiple time series are reconstructed based on the phase space reconstruction theory using the time delay obtained by mutual information method and the embedding dimension captured by the maximum conditional entropy method. Finally, the reconstructed phase space is used as the input and the support vector regression optimized by the genetic algorithm is utilized to predict the traffic flow. Numerical experiments are performed and the results show that the approach proposed has strong fitting capability and better prediction accuracy.

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