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Frontiers of Computer Science 2021年第1期15卷 99-111页

作者： Zhiyong YU Xiangping ZHENG Fangwan HUANG Wenzhong GUO Lin SUN Zhiwen YU College of Mathematics and Computer Science Fuzhou UniversityFuzhou 350108China Hangzhou Key Laboratory for IoT Technology&Application Zhejiang University City CollegeHangzhou 310015China School of Computer Science Northwestern Polytechnical UniversityXi’an 710072China

Smart city driven by Big Data and Internet of Things(loT)has become a most promising trend of the *** one important function of smart city,event alert based on time series prediction is faced with the challenge of how to extract and represent discriminative features of sensing knowledge from the massive sequential data generated by IoT *** this paper,a framework based on sparse representa-tion model(SRM)for time series prediction is proposed as an efficient approach to tackle this *** dividing the over-complete dictionary into upper and lower parts,the main idea of SRM is to obtain the sparse representation of time series based on the upper part firstly,and then realize the prediction of future values based on the lower *** choice of different dictionaries has a significant impact on the performance of *** paper focuses on the study of dictionary construction strategy and summarizes eight variants of *** results demonstrate that SRM can deal with different types of time series prediction flexibly and effectively.

关键词： sparse representation smart city time series prediction dictionary construction

Application of uncertainty reasoning based on cloud model in time series prediction

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Journal of Zhejiang University Science 2003年第5期4卷 578-583页

作者：张锦春胡谷雨 InstituteolcCommandAutomation PLAUniversityofScienceandTechnologyNanjing210007China

time series prediction has been successfully used in several application areas, such as meteoro-logical forecasting, market prediction, network traffic forecasting, etc. , and a number of techniques have been developed for modeling and predicting time series. In the traditional exponential smoothing method, a fixed weight is assigned to data history, and the trend changes of time series are ignored. In this paper, an uncertainty reasoning method, based on cloud model, is employed in time series prediction, which uses cloud logic controller to adjust the smoothing coefficient of the simple exponential smoothing method dynamically to fit the current trend of the time series. The validity of this solution was proved by experiments on various data sets.

关键词： time series prediction Cloud model Simple expo nential smoothing method

Parameter selection in time series prediction based on nu-support vector regression

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High Technology Letters 2009年第4期15卷 337-342页

作者：胡亮 Che Xilong College of Computer Science and Technology Jilin University Changchun 130012 P. R. China

The theory of nu-support vector regression (Nu-SVR) is employed in modeling time series variationfor prediction. In order to avoid prediction performance degradation caused by improper parameters, themethod of parallel multidimensional step search (PMSS) is proposed for users to select best parameters intraining support vector machine to get a prediction model. A series of tests are performed to evaluate themodeling mechanism and prediction results indicate that Nu-SVR models can reflect the variation tendencyof time series with low prediction error on both familiar and unfamiliar data. Statistical analysis is alsoemployed to verify the optimization performance of PMSS algorithm and comparative results indicate thattraining error can take the minimum over the interval around planar data point corresponding to selectedparameters. Moreover, the introduction of parallelization can remarkably speed up the optimizing procedure.

关键词： parameter selection time series prediction nu-support vector regression (Nu-SVR) parallel multidimensional step search (PMSS)

Chaotic time series prediction using least squares support vector machines

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Chinese Physics B 2004年第4期13卷 454-458页

作者：叶美盈汪晓东 CollegeofMathematicsandPhysics ZhejiangNormalUniversityJinhua321004China CollegeofInformationScienceandEngineering ZhejiangNormalUniversityJinhua321004China

We propose a new technique of using the least squares support vector machines (LS-SVMs) for making one-step and multi-step prediction of chaotic time series. The LS-SVM achieves higher generalization performance than traditional neural networks and provides an accurate chaotic time series prediction. Unlike neural networks' training that requires nonlinear optimization with the danger of getting stuck into local minima, training LS-SVM is equivalent to solving a set of linear equations. Thus it has fast convergence. The simulation results show that LS-SVM has much better potential in the field of chaotic time series prediction.

关键词： chaotic time series time series prediction support vector machines

LS-SVR and AGO Based time series prediction Method

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International Journal of Plant Engineering and Management 2016年第1期21卷 1-13页

作者： ZHANG Shou-peng LIU Shan CHAI Wang-xu ZHANG Jia-qi GUO Yang-ming Aeronautical Computing Technique Research Institute Aviation Industry Corporation of China School of Computer Science and Technology Northwestern Polytechnical University

Recently, fault or health condition prediction of complex systems becomes an interesting research topic. However, it is difficult to establish precise physical model for complex systems, and the time series properties are often necessary to be incorporated for the prediction in practice. Currently, the LS-SVR is widely adopted for prediction of systems with time series data. In this paper, in order to improve the prediction accuracy, accumulated generating operation （AGO） is carried out to improve the data quality and regularity of raw time series data based on grey system theory; then, the inverse accumulated generating operation （IAGO） is performed to obtain the prediction results. In addition, due to the reason that appropriate kernel function plays an important role in improving the accuracy of prediction through LS-SVR, a modified Gaussian radial basis function （RBF） is proposed. The requirements of distance functions-based kernel functions are satisfied, which ensure fast damping at the place adjacent to the test point and a moderate damping at infinity. The presented model is applied to the analysis of benchmarks. As indicated by the results, the proposed method is an effective prediction one with good precision.

关键词： time series prediction least squares support vector regression (LS-SVR) Gaussian radial basisfunction (RBF) accumulated generating operation (AGO)

time series online prediction algorithm based on least squares support vector machine

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Journal of Central South University of Technology 2007年第3期14卷 442-446页

作者：吴琼刘文颖杨以涵 Key Laboratory of Power System Protection and Dynamic Security Monitory and Control of Ministry of Education North China Electric Power University

Deficiencies of applying the traditional least squares support vector machine （LS-SVM） to time series online prediction were specified. According to the kernel function matrix＇s property and using the recursive calculation of block matrix, a new time series online prediction algorithm based on improved LS-SVM was proposed. The historical training results were fully utilized and the computing speed of LS-SVM was enhanced. Then, the improved algorithm was applied to timc series online prediction. Based on the operational data provided by the Northwest Power Grid of China, the method was used in the transient stability prediction of electric power system. The results show that, compared with the calculation time of the traditional LS-SVM（75 1 600 ms）, that of the proposed method in different time windows is 40-60 ms, proposed method is above 0.8. So the improved method is online prediction. and the prediction accuracy（normalized root mean squared error） of the better than the traditional LS-SVM and more suitable for time series online prediction.

关键词： time series prediction machine learning support vector machine statistical learning theory

Online Sequential Extreme Multilayer Perception with time series Learning Machine Based Output Self Feedback for prediction

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Journal of Shanghai Jiaotong university(Science) 2013年第3期18卷 366-375页

作者： PAN Feng ZHAO Hai-bo School of Information Science and Technology Donghua University Shanghai 200051 China Institude of Aircraft Equipment Naval Academy of Armament Shanghai 200436 China

This study presents a time series prediction model with output self feedback which is implemented based on online sequential extreme learning machine. The output variables derived from multilayer perception can feedback to the network input layer to create a temporal relation between the current node inputs and the lagged node outputs while overcoming the limitation of memory which is a vital port for any time-series prediction application. The model can overcome the static prediction problem with most time series prediction models and can effectively cope with the dynamic properties of time series data. A linear and a nonlinear forecasting algorithms based on online extreme learning machine are proposed to implement the output feedback forecasting model. They are both recursive estimator and have two distinct phases： Predict and Update. The proposed model was tested against different kinds of time series data and the results indicate that the model outperforms the original static model without feedback.

关键词： time series prediction extreme learning machine (ELM) autoregression (AR) online sequential learning ELM (OS-ELM) recurrent neural network (RNN)

STUDY ON THE prediction METHOD OF LOW-DIMENSION time series THAT ARISE FROM THE INTRINSIC NONLINEAR DYNAMICS

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Applied Mathematics and Mechanics(English Edition) 2001年第5期22卷 501-509页

作者： MA Junhai(马军海) CHEN Yushu(陈予恕) School of Management Tianjin University Tianjin 300072 P R China Department of Mechanics Tianjin University Tianjin 300072 P R China

The prediction methods and its applications of the nonlinear dynamic systems determined from chaotic time series of low-dimension are discussed mainly. Based on the work of the foreign researchers, the chaotic time series in the phase space adopting one kind of nonlinear chaotic model were reconstructed. At first, the model parameters were estimated by using the improved least square method. Then as the precision was satisfied, the optimization method was used to estimate these parameters. At the end by using the obtained chaotic model, the future data of the chaotic time series in the phase space was predicted. Some representative experimental examples were analyzed to testify the models and the algorithms developed in this paper. ne results show that if the algorithms developed here are adopted, the parameters of the corresponding chaotic model will be easily calculated well and true. predictions of chaotic series in phase space make the traditional methods change from outer iteration to interpolations. And if the optimal model rank is chosen, the prediction precision will increase notably. Long term superior predictability of nonlinear chaotic models is proved to be irrational and unreasonable.

关键词： nonlinear chaotic model parameter identification time series prediction

STUDY ON prediction METHODS FOR DYNAMIC SYSTEMS OF NONLINEAR CHAOTIC time series＊

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Applied Mathematics and Mechanics(English Edition) 2004年第6期25卷 605-611页

作者：马军海陈予恕辛宝贵 Management School Tianjin UniversityTianjin300072P.R.China Tianjin University of Finance and Economics Tianjin300222P.R.China Mechanics Department Tianjin Universitytianjin300072P.R.China Tianjin University of Finance and Economics Tianjin300222P.R.China

The prediction methods for nonlinear dynamic systems which are decided by chaotic time series are mainly studied as well as structures of nonlinear self-related chaotic models and their dimensions. By combining neural networks and wavelet theories, the structures of wavelet transform neural networks were studied and also a wavelet neural networks learning method was given. Based on wavelet networks, a new method for parameter identification was suggested, which can be used selectively to extract different scales of frequency and time in time series in order to realize prediction of tendencies or details of original time series. Through pre-treatment and comparison of results before and after the treatment, several useful conclusions are reached: High accurate identification can be guaranteed by applying wavelet networks to identify parameters of self-related chaotic models and more valid prediction of the chaotic time series including noise can be achieved accordingly.

关键词： nonlinear self-related chaotic model wavelet neural network parameter identification time series prediction