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Ergodicity of a Class of nonlinear time series Models in Random Environment Domain

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Acta Mathematicae Applicatae Sinica 2010年第1期26卷 159-168页

作者： En-wen Zhu Han-jun Zhang Gang Yang Zai-ming Liu Jie-zhong Zou Shao-shun Long School of Mathematics and Computational Science Xiangtan University Hunan 411105 China School of Mathematics and Computing Sciences Changsha University of Science and Technology Hunan410076 China School of Information Hunan University of Commerce Hunan 410205 China School of Mathematics Central South University Hunan 410075 China

In this paper, we study the problem of a variety of p, onlinear time series model Xn＋ 1= TZn＋1（X（n）, … ,X（n - Zn＋l）, en＋1（Zn＋1）） in which {Zn} is a Markov chain with finite state space, and for every state i of the Markov chain, {en（i）} is a sequence of independent and identically distributed random variables. Also, the limit behavior of the sequence {Xn} defined by the above model is investigated. Some new novel results on the underlying models are presented.

关键词： Ergodicity Random environment nonlinear time series

Discussion About nonlinear time series Prediction Using Least Squares Support Vector Machine

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Communications in Theoretical Physics 2005年第6期43卷 1056-1060页

作者： XURui-Rui BIANGuo-Xin GAOChen-Feng CHENTian-Lun DepartmentofPhysics NankaiUniversityTianjin300071China

The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter gamma and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values.. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.

关键词： least squares support vector machine nonlinear time series prediction clustering

Discussion of Some Problems About nonlinear time series Prediction Using v-Support Vector Machine

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Communications in Theoretical Physics 2007年第7期48卷 117-124页

作者： GAO Cheng-Feng CHEN Tian-Lun NAN Tian-Shi Department of Physics,Nankai University,Tianjin 300071,China Department of Physics Nankai University Tianjin 300071 China

Some problems in using v-support vector machine(v-SVM)for the prediction of nonlinear time series *** problems include selection of various net parameters,which affect the performance of prediction,mixtureof kernels,and decomposition cooperation linear programming v-SVM regression,which result in improvements of *** simulations in the prediction of nonlinear time series produced by Mackey-Glass equation andLorenz equation provide some improved results.

关键词： v-SVM nonlinear time series prediction

Determining the minimum embedding dimension of nonlinear time series based on prediction method

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Chinese Physics B 2004年第5期13卷 633-636页

作者：卞春华宁新宝 StateKeyLaboratoryofModernAcoustics InstituteforBiomedicalElectronicEngineeringNanjingUniversityNanjing210093China

Determining the embedding dimension of nonlinear time series plays an important role in the reconstruction of nonlinear dynamics. The paper first summarizes the current methods for determining the embedding ***, inspired by the fact that the optimum modelling dimension of nonlinear autoregressive (NAR) prediction model can characterize the embedding feature of the dynamics, the paper presents a new idea that the optimum modelling dimension of the NAR model can be taken as the minimum embedding dimension. Some validation examples and results are given and the present method shows its advantage for short data series.

关键词： nonlinear time series embedding dimension NAR model prediction5. Conclusion

Determining the input dimension of a neural network for nonlinear time series prediction

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Chinese Physics B 2003年第6期12卷 594-598页

作者：张胜刘红星高敦堂都思丹 DepartmentofElectronicScienceandEngineering NanjingUniversitynanjing210093China DepartmentofPhysics nanjingNormalUniversityNanjing210097China DepartmentofElectronicScienceandEngineering NanjingUniversitynanjing210093China

Determining the input dimension of a feed-forward neural network for nonlinear time series prediction plays an important role in the *** paper first summarizes the current methods for determining the input dimension of the neural *** inspired by the fact that the correlation dimension of a nonlinear dynamic system is the mostimportant feature of it,the paper presents a new idea that the input dimension of the neural network for nonlinear time series prediction can be taken as an integer just greater than or equal to the correlation ***,some wlidation examples and results are given.

关键词： nonlinear time series prediction phase space reconstruction neural network input dimension

A Prediction Method Based on Improved Echo State Network for COVID-19 nonlinear time series

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Journal of Computer and Communications 2020年第12期8卷 113-122页

作者： Banteng Liu Wei Chen Yourong Chen Ping Sun Heli Jin Hao Chen College of Information Science and Technology Zhejiang Shuren University Hangzhou China School of Computer Science and Artificial Intelligence Changzhou University Changzhou China

This paper proposes a prediction method based on improved Echo State Network for COVID-19 nonlinear time series, which improves the Echo State Network from the reservoir topology and the output weight matrix, and adopt the ABC (Artificial Bee Colony) algorithm based on crossover and crowding strategy to optimize the parameters. Finally, the proposed method is simulated and the results show that it has stronger prediction ability for COVID-19 nonlinear time series.

关键词： COVID-19 nonlinear time series Prediction Echo State Network

A modified Markov transition probability-based network constructing method and its application on nonlinear time series analysis

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A modified Markov transition probability-based network const...

A Prediction Method Based on Improved Echo State Network for COVID-19 nonlinear time series

第32届中国控制与决策会议

作者： LIU Jie WANG Hao XU Huibin BAO Shengqiao LI Li Research Center of nonlinear Science Wuhan Textile University Mathematics Department Wuhan Textile University

Under the view point of complex networks,a Markov chain can be described by a weight directed graph,in which the weight of different edges is labeled by the Markov transfer probabilities of jumping from current state to the future *** original quantile graph method is based on the Markov transition probability with equal number of existing data points *** ignored the amplitude information during constructing the associate *** this brief paper,a modified quantile graph constructing method based on the Markov transition probability with equal distances interval dividing strategy is *** analysis of two algorithms in characterizing nonlinear time series are carried out based on the same EEG database,i.e.,comparison of these two algorithms with each other were discussed via being applied on real data classification *** is suggested that these two methods can be chosen for the analysis of nonlinear time series with uniform distributions or non-uniform distributions,respectively.

关键词： nonlinear time series Complex networks Markov transition probability quantile graph method

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A Prediction Method Based on Improved Echo State Network for...

作者： Banteng Liu Wei Chen Yourong Chen Ping Sun Heli Jin Hao Chen College of Information Science and Technology Zhejiang Shuren University School of Computer Science and Artificial Intelligence Changzhou University

This paper proposes a prediction method based on improved Echo State Network for COVID-19 nonlinear time series, which improves the Echo State Network from the reservoir topology and the output weight matrix, and adopt the ABC（Artificial Bee Colony） algorithm based on crossover and crowding strategy to optimize the parameters. Finally, the proposed method is simulated and the results show that it has stronger prediction ability for COVID-19 nonlinear time series.

关键词： COVID-19 nonlinear time series Prediction Echo State Network

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基于核密度估计的非线性时间序列聚类