Algorithms Research in the Application of Lorenz Time Series Prediction

Prediction of Lorenz Chaotic Time Series is a vital problem in nonlinear dynamics .Support vector machine (SVM) is a kind of novel machine learning methods based on statistical learning theory, which have been provided an efficient algorithm thought in prediction of Chaotic Time Series. This paper combined SVM with neural network which based on the similarity of structure between SVM and RBF Networks, using SVM to obtain the centers of RBF Networks, then to predict the Lorenz Chaotic Time Series. Simulation results show that the effect is better than other methods.

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Chaotic time series prediction based on multi-kernel learning support vector regression

Acta Physica Sinica ◽

10.7498/aps.57.2708 ◽

2008 ◽

Vol 57 (5) ◽

pp. 2708

Author(s):

Zhang Jun-Feng ◽

Hu Shou-Song

Keyword(s):

Time Series ◽

Support Vector Regression ◽

Time Series Prediction ◽

Chaotic Time Series ◽

Kernel Learning ◽

Support Vector ◽

Learning Support ◽

Chaotic Time Series Prediction

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Chaotic time series prediction using knowledge based Green’s Kernel and least-squares support vector machines

2007 IEEE International Conference on Systems, Man and Cybernetics ◽

10.1109/icsmc.2007.4414023 ◽

2007 ◽

Cited By ~ 7

Author(s):

Tahir Farooq ◽

Aziz Guergachi ◽

Sridhar Krishnan

Keyword(s):

Time Series ◽

Support Vector Machines ◽

Least Squares ◽

Time Series Prediction ◽

Chaotic Time Series ◽

Support Vector ◽

Knowledge Based ◽

Vector Machines ◽

Chaotic Time Series Prediction

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Study on Prediction of Chaotic Time Series Using Least Squares Support Vector Machines

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1061-1062.935 ◽

2014 ◽

Vol 1061-1062 ◽

pp. 935-938

Author(s):

Xin You Wang ◽

Guo Fei Gao ◽

Zhan Qu ◽

Hai Feng Pu

Keyword(s):

Time Series ◽

Support Vector Machine ◽

Support Vector Machines ◽

Least Squares ◽

Prediction Accuracy ◽

Chaotic Time Series ◽

Support Vector ◽

Vector Machines ◽

Online Prediction ◽

Better Than

The predictions of chaotic time series by applying the least squares support vector machine (LS-SVM), with comparison with the traditional-SVM and-SVM, were specified. The results show that, compared with the traditional SVM, the prediction accuracy of LS-SVM is better than the traditional SVM and more suitable for time series online prediction.

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Study on Prediction of Mackey-Glass Chaotic Time Series Using Support Vector Machines

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1051.1009 ◽

2014 ◽

Vol 1051 ◽

pp. 1009-1015 ◽

Cited By ~ 2

Author(s):

Ya Li Ning ◽

Xin You Wang ◽

Xi Ping He

Keyword(s):

Machine Learning ◽

Time Series ◽

Support Vector Machines ◽

Statistical Learning ◽

Chaotic Time Series ◽

Support Vector ◽

Machine Type ◽

Type Selection ◽

Vector Machines ◽

New Generation

Support Vector Machines (SVM), which is a new generation learning method based on advances in statistical learning theory, is characterized by the use of many standard technologies of machine learning such as maximal margin hyperplane, Mercel kernels and the quadratic programming. Because the best performance is obtained in many currently challenging applications, SVM has sustained wide attention, and has been become the standard tools of machine learning and data mining. But as a developing technology, SVM still have some problems and its applications are limited. In this paper, SVM and its applications in chaotic time series including predicting chaotic time series, focus on comparison in regression type selection, and kernel type selection in the same regression machine type.

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Application of least square support vector machine based on particle swarm optimization to chaotic time series prediction

2009 IEEE International Conference on Intelligent Computing and Intelligent Systems ◽

10.1109/icicisys.2009.5357656 ◽

2009 ◽

Cited By ~ 1

Author(s):

Ping Liu ◽

Jian Yao

Keyword(s):

Time Series ◽

Support Vector Machine ◽

Particle Swarm Optimization ◽

Time Series Prediction ◽

Particle Swarm ◽

Chaotic Time Series ◽

Least Square ◽

Support Vector ◽

Swarm Optimization ◽

Chaotic Time Series Prediction

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FPGA-Based Implementation of a Multilayer Perceptron Suitable for Chaotic Time Series Prediction

Technologies ◽

10.3390/technologies6040090 ◽

2018 ◽

Vol 6 (4) ◽

pp. 90 ◽

Cited By ~ 12

Author(s):

Ana Pano-Azucena ◽

Esteban Tlelo-Cuautle ◽

Sheldon Tan ◽

Brisbane Ovilla-Martinez ◽

Luis de la Fraga

Keyword(s):

Time Series ◽

Multilayer Perceptron ◽

Time Series Data ◽

Mean Squared Error ◽

Time Series Prediction ◽

Chaotic Time Series ◽

Series Data ◽

Support Vector ◽

Maximum Lyapunov Exponent ◽

Inference System

Many biological systems and natural phenomena exhibit chaotic behaviors that are saved in time series data. This article uses time series that are generated by chaotic oscillators with different values of the maximum Lyapunov exponent (MLE) to predict their future behavior. Three prediction techniques are compared, namely: artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector machines (SVM). The experimental results show that ANNs provide the lowest root mean squared error. That way, we introduce a multilayer perceptron that is implemented using a field-programmable gate array (FPGA) to predict experimental chaotic time series.

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