水质时间序列数据挖掘及其应用集成研究
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摘要
随着长江三峡库区蓄水位不断升高,给库区水环境质量带来了巨大的影响,为更进一步掌握库区水环境质量状况以确保库区水环境安全,环保部门逐步建立了愈来愈完善的三峡库区水质监测系统,获得了大量的水质时间序列数据。由于时间序列数据本身所具备的高维性、复杂性、动态性、高噪声特性以及容易达到大规模的特性,急需一种方法从这些数据中发现水质变化规律和分布状况。
本文以时间序列数据挖掘为基础理论,以重庆市科技攻关重点项目(CSTC,2006AA7024)“三峡库区水环境安全预警平台与辅助决策关键技术研究”为应用背景,针对水环境中水质监测时间序列数据,围绕时间序列数据挖掘的理论与方法研究,从时间序列模式表示、多维时间序列相似度量、时间序列预测和时间序列数据挖掘模型与预警平台的应用集成四方面开展以下研究工作:
①分析时间序列的模式表示,着重讨论时间序列的分段模式表示,通过分析分段线性化与分段多项式的基本思想,将分段线性化全局连续性的优点与分段多项式局部形态保持的优点结合起来,提出一种时间序列分段多项式连续模式表示方法。实验表明,时间序列分段多项式连续模式表示在保留分段多项式在局部形态上优点的同时具备分段线性化的全局连续性,且能够与分段线性化模式表示兼容,更好地保留时间序列形态。该算法具有的这种时间序列形态保持性,能够作为时间序列的趋势提取和噪声过滤的基本算法,可以推广应用于水环境安全领域中水质时间序列数据预处理过程。
②在对一维时间序列Lp距离和DTW距离的相似性度量分析的基础上,研究将空间路径相似引入多维时间序列的相似性度量中,通过空间路径的相似性度量确定多维时间序列的相似性,提出基于路径DTW相似的多维时间序列相似性度量,并将其应用于多维时间序列聚类。通过实验将基于一维相似累加、基于路径欧氏距离相似和基于路径DTW相似的多维时间序列聚类效果进行比较,结果表明在多维时间序列聚类上,以路径DTW相似聚类能完全正确分类,路径欧氏距离相似聚类只在差距较小的多维时间序列间存在误分,效果都优于一维相似累加聚类。基于路径DTW相似的多维时间序列聚类用于水环境河流分类,获得了较好的实际应用效果。
③针对时间序列神经网络预测的过拟合问题,研究RBF和神经网络集成的基本原理,结合PCA技术与样本聚类技术,提出一种时间序列RBF神经网络集成预测的方法。在时间序列分割后形成样本的基础之上,使用PCA技术得到新样本,以新样本维数为个体RBF网络输入维数,顺序选择新样本簇的中心和半径为个体RBF的中心参数,将个体RBF对输入/输出的先验知识引入到平均集成中。实验表明,该方法的时间序列预测精度高于任意个体RBF的预测精度,将该方法应用于水环境水质预测,获得了较好的实际应用效果。
④研究时间序列数据挖掘与三峡库区水环境安全预警平台的应用集成方法。以服务请求的应用集成机制为基础,设计了基于服务请求的应用集成结构,及其开放的集成计算服务器结构,同时定义了应用客户端与集成计算服务基于XML的模型查询、模型目录、计算请求和计算结果的服务请求协议。
本文所研究的时间序列数据挖掘能有效地进行水质趋势提取以及噪声过滤、根据水质各指标对河流进行分类和水质预测;设计的开放式集成计算服务器结构,不但能够支持水质模型服务的扩展,同时也能满足其它系统或平台对这些模型的需求,并通过实际应用验证了方法的可行性。
As the water level of Three Gorges Reservoir rising, it has brought great influence to the reservoir water quality. To further grasp the reservoir water quality conditions to ensure the water environment safety of the reservoir, The environmental protection departments has gradually established more and more water quality monitoring systems for the Three Gorges Reservoir, and got a lot of water quality time series data. We needed a way to find variation and distribution of water quality from these data, due to the characteristics of time series data itself such as the high dimensional, complex, dynamic, high noise, and easy to achieve large-scale.
Based on the theory of the Time Series Data Mining (TSDM) and supported by project of the ChongQing Science&Technology Committee: "Research on Water Environment Security Early Warning Platform and Key Technology of Science Decision in Three Gorge Region "(CSTC2006AA7024), this research, based on the time series data in water quality monitoring of environmental and focusing on the method and theory of the TSDM, was carried out from the following four perspectives: the time series representation, similarity measures of Multivariate Time Series (MTS), time series forecast and application integration.
①This paper analyzed the representation of time series and focus on piecewise representation. The author present a piecewise polynomial continuous representation, through analysing the basic theory of piecewise linear and piecewise polynomial, overall the advantages of global continuity of the piecewise linear and local shape retain of piecewise polynomial. As the simulation results indicated that the continuous piecewise polynomial representation of time series retained the advantages of piecewise polynomials in the fitting, owned the advantages of continuity with piecewise linear, and was compatible with the piecewise linear, retained most representative of the time series characteristics. Form maintaining of time series in this algorithm enables it to be used to the trends extract and noise filter for the time series and applied to pre-process time series data in the field of water safety.
②The author introduced the similarity of space path into MTS similarity measure based on the analyzing of the similarity measure between the distance of STS and the DTW distance, and propose a clustering method for the MTS based on the path similarity through measure the similarity of the space path to determine the similarity of MTS. The author compared the clustering result based on the path similarity of DTW with the clustering result based on the similarity of Euclidean distance and MTS clustering which is STS similar. The result of comparing shows that the clustering method based on DWT similarity can cluster data in the multidimensional time series completely correct, and the method based on the similarity of path Euclidean distance exist error just in the MTS which their difference is smaller, the two methods of clustering are all better than method based on theSTS. The better effect of practical applications was got when the method of clustering based on the DTW similar path or multivariate time series used in the river clustering of water environment.
③In view of the forecast problem in neural networks for time series, the author research the basic principles of RBF and neural network integration, combine PCA technique and sample cluster center, proposed a RBF neural network integration method. The idea of the RBF neural network integration is using the dimension of the sample which is split from time series with the technology of PCA as the input dimension of Individual RBF network, orderly select the cluster center and radius of the sample as the individual RBF parameters, and introduce the priori knowledge into the average ensemble. Through the experiment, the result shows that the forecast accuracy of the RBF neural network integration method is higher than any individual in the forecast accuracy of RBF and the method get better application effect when using the method in the water quality forecast of water environment.
④The author design an application structure based on the service request mechanisms and an open TSDM calculation server architecture through combining the service request mechanism and TSDM method, and define the service request protocols for the client and the compute service, the protocols contain module querying, module contents, compute request and compute result, which are all based on the XML.
The water quality time series data mining researched in this paper can extract effectively trends and filter noise, classify the rivers and forecast water quality. The open computing servers open integration structures, water quality model not only can support the expansion of services, while can also meet other system or platform, the demand for these models, and verified through the practical application of the method is feasible.
本文以时间序列数据挖掘为基础理论,以重庆市科技攻关重点项目(CSTC,2006AA7024)“三峡库区水环境安全预警平台与辅助决策关键技术研究”为应用背景,针对水环境中水质监测时间序列数据,围绕时间序列数据挖掘的理论与方法研究,从时间序列模式表示、多维时间序列相似度量、时间序列预测和时间序列数据挖掘模型与预警平台的应用集成四方面开展以下研究工作:
①分析时间序列的模式表示,着重讨论时间序列的分段模式表示,通过分析分段线性化与分段多项式的基本思想,将分段线性化全局连续性的优点与分段多项式局部形态保持的优点结合起来,提出一种时间序列分段多项式连续模式表示方法。实验表明,时间序列分段多项式连续模式表示在保留分段多项式在局部形态上优点的同时具备分段线性化的全局连续性,且能够与分段线性化模式表示兼容,更好地保留时间序列形态。该算法具有的这种时间序列形态保持性,能够作为时间序列的趋势提取和噪声过滤的基本算法,可以推广应用于水环境安全领域中水质时间序列数据预处理过程。
②在对一维时间序列Lp距离和DTW距离的相似性度量分析的基础上,研究将空间路径相似引入多维时间序列的相似性度量中,通过空间路径的相似性度量确定多维时间序列的相似性,提出基于路径DTW相似的多维时间序列相似性度量,并将其应用于多维时间序列聚类。通过实验将基于一维相似累加、基于路径欧氏距离相似和基于路径DTW相似的多维时间序列聚类效果进行比较,结果表明在多维时间序列聚类上,以路径DTW相似聚类能完全正确分类,路径欧氏距离相似聚类只在差距较小的多维时间序列间存在误分,效果都优于一维相似累加聚类。基于路径DTW相似的多维时间序列聚类用于水环境河流分类,获得了较好的实际应用效果。
③针对时间序列神经网络预测的过拟合问题,研究RBF和神经网络集成的基本原理,结合PCA技术与样本聚类技术,提出一种时间序列RBF神经网络集成预测的方法。在时间序列分割后形成样本的基础之上,使用PCA技术得到新样本,以新样本维数为个体RBF网络输入维数,顺序选择新样本簇的中心和半径为个体RBF的中心参数,将个体RBF对输入/输出的先验知识引入到平均集成中。实验表明,该方法的时间序列预测精度高于任意个体RBF的预测精度,将该方法应用于水环境水质预测,获得了较好的实际应用效果。
④研究时间序列数据挖掘与三峡库区水环境安全预警平台的应用集成方法。以服务请求的应用集成机制为基础,设计了基于服务请求的应用集成结构,及其开放的集成计算服务器结构,同时定义了应用客户端与集成计算服务基于XML的模型查询、模型目录、计算请求和计算结果的服务请求协议。
本文所研究的时间序列数据挖掘能有效地进行水质趋势提取以及噪声过滤、根据水质各指标对河流进行分类和水质预测;设计的开放式集成计算服务器结构,不但能够支持水质模型服务的扩展,同时也能满足其它系统或平台对这些模型的需求,并通过实际应用验证了方法的可行性。
As the water level of Three Gorges Reservoir rising, it has brought great influence to the reservoir water quality. To further grasp the reservoir water quality conditions to ensure the water environment safety of the reservoir, The environmental protection departments has gradually established more and more water quality monitoring systems for the Three Gorges Reservoir, and got a lot of water quality time series data. We needed a way to find variation and distribution of water quality from these data, due to the characteristics of time series data itself such as the high dimensional, complex, dynamic, high noise, and easy to achieve large-scale.
Based on the theory of the Time Series Data Mining (TSDM) and supported by project of the ChongQing Science&Technology Committee: "Research on Water Environment Security Early Warning Platform and Key Technology of Science Decision in Three Gorge Region "(CSTC2006AA7024), this research, based on the time series data in water quality monitoring of environmental and focusing on the method and theory of the TSDM, was carried out from the following four perspectives: the time series representation, similarity measures of Multivariate Time Series (MTS), time series forecast and application integration.
①This paper analyzed the representation of time series and focus on piecewise representation. The author present a piecewise polynomial continuous representation, through analysing the basic theory of piecewise linear and piecewise polynomial, overall the advantages of global continuity of the piecewise linear and local shape retain of piecewise polynomial. As the simulation results indicated that the continuous piecewise polynomial representation of time series retained the advantages of piecewise polynomials in the fitting, owned the advantages of continuity with piecewise linear, and was compatible with the piecewise linear, retained most representative of the time series characteristics. Form maintaining of time series in this algorithm enables it to be used to the trends extract and noise filter for the time series and applied to pre-process time series data in the field of water safety.
②The author introduced the similarity of space path into MTS similarity measure based on the analyzing of the similarity measure between the distance of STS and the DTW distance, and propose a clustering method for the MTS based on the path similarity through measure the similarity of the space path to determine the similarity of MTS. The author compared the clustering result based on the path similarity of DTW with the clustering result based on the similarity of Euclidean distance and MTS clustering which is STS similar. The result of comparing shows that the clustering method based on DWT similarity can cluster data in the multidimensional time series completely correct, and the method based on the similarity of path Euclidean distance exist error just in the MTS which their difference is smaller, the two methods of clustering are all better than method based on theSTS. The better effect of practical applications was got when the method of clustering based on the DTW similar path or multivariate time series used in the river clustering of water environment.
③In view of the forecast problem in neural networks for time series, the author research the basic principles of RBF and neural network integration, combine PCA technique and sample cluster center, proposed a RBF neural network integration method. The idea of the RBF neural network integration is using the dimension of the sample which is split from time series with the technology of PCA as the input dimension of Individual RBF network, orderly select the cluster center and radius of the sample as the individual RBF parameters, and introduce the priori knowledge into the average ensemble. Through the experiment, the result shows that the forecast accuracy of the RBF neural network integration method is higher than any individual in the forecast accuracy of RBF and the method get better application effect when using the method in the water quality forecast of water environment.
④The author design an application structure based on the service request mechanisms and an open TSDM calculation server architecture through combining the service request mechanism and TSDM method, and define the service request protocols for the client and the compute service, the protocols contain module querying, module contents, compute request and compute result, which are all based on the XML.
The water quality time series data mining researched in this paper can extract effectively trends and filter noise, classify the rivers and forecast water quality. The open computing servers open integration structures, water quality model not only can support the expansion of services, while can also meet other system or platform, the demand for these models, and verified through the practical application of the method is feasible.
引文
[1] U.Fayyed,GP.Shapino,P.Smyth.From data mining to knowledge discovery in database [EB/OL].2003.
[2] E.Keogh.Data mining and machine learning in time series database[C].Proc of 5th Industrial Conference on Data Mining (ICDM),2005.
[3] HAN Jiawei,M.Kamber.Data mining concepts and techniques[M].New York: Morgan Kaufmann Publishers,2000:7-9.
[4] M.Antunes,L.Oliveira.Temporal Data Mining: an overview[C].KDD 2001 Workshop on Temporal Data Mining,2001.
[5] F.M?rchen.Time Series Knowledge Mining[C].Phd Thesis Philipps University Marburg Germany,G?rich & Weiersh?user Marburg,Germany,2006:180-187.
[6] Yang Qiang,Wu Xindong.10 challenging problems in data mining research[J].Inernational Journal of Information Technology & Decision Making,2006,5(4):597-604.
[7] R.Agrawal, C.Faloutsos,A.Swami. Efficient similarity search in sequence databases[C]. Proc of 4th International Conference on Foundations of Data Organization and Algorithms,London:Springer-Verlag,1993:69-84.
[8] C.Faloutsos,M.Ranganathan,Y.Manolopoules.Fast subsequence matching in time-series database[C].Proc of the ACM SIGMOD International Conference on Management of Data, Minepolist:ACM Press,1994:419-429.
[9]潘定,沈君疑.时态数据挖掘的相似性发现技术[J].软件学报,2007,18(2):246-258.
[10] Lin Weiqiang,MA.Orgun,GL.Williams.An overview of temporal data mining[C].Proc of the 1st Australian Data Mining Workshop,Canberra:University of Technology,2002:83-90.
[11] K.Hoppner.Time series abstraction methods:a survey[C].Proc of Jahrestagung Informatics Workshop on Knowledge Discovery in Databases,Dertmund,2002:777-786.
[12]李爱国.时间序列数据分割与时态模式挖掘研究[D].西安:西安交通大学,2003:2-4.
[13] S.Laxman,PS.Sastry.A Surrey of temporal data mining[J].Sadhama Academy Proceedings in Engineering Science,2006,31(2):173-198.
[14] Wang Changzhou,Wang Xiaoyang.Multilevel filtering for high dimensional nearest neighbor search[C].Proc of ACM SIGMOD workshop on n Research issues in data mining andknowledge discovery,Dallsa ACM Press,2000:37-43.
[15] I.Popivanov,RJ.Miller.Similarity search over time-series data using wavelets[C].Proc of the 18th International Conference on Data Engineering San Jose.IEEE Computer Society Press,2002:212-221.
[16]张海琴,蔡庆生.基于小波变换的时间序列相似模式匹配[J].计算机学报,2003,26(3):373-377.
[17] E.Keogh,Chakrabartik,Pazzanim,et al.Dimensionality reduction for fast similarity search in large time series databases[J].Journal of Knowledge and Information Systems,2001,3(3):263-286.
[18] Gex , P . Smyth . Deformable markov model templates for time series pattern matching[C].Proc of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,Boston ACM Press,2000:81-89.
[19] B.K Yi,C.Faloustsos.Fast Time Sequence Indexing for Arbitrary Lp Norms[C].Proc of the 26th International Conference on Very Large Data Bases,San Francisco:Morgan Kaufmann Publishers Inc,2000:385-394.
[20] E.Keogh,K.Chakrabarti,M.Pazzani,et al.Dimensionality Reeducation for similarity search in Larg Time Series Databases[J].Knowledge and Information Systems,2001,8(3).
[21] CS.Perng,H.Wang,S.Zhang,et al.Landmarks:A new model for similarity-based patternquerying in time series databases[C].In:Data Engineering Proceedings,16th International Conference on,2000:33-42.
[22]李爱国,覃征.在线分割时间序列数据[J].软件学报,2004,15(11):1671-1679.
[23]周大镯,李敏强.基于序列重要点的时间序列分割[J].计算机工程,2008,34(23):14-16.
[24]尚福华,孙达辰.基于时间序列趋势转折点的分段线性表示[J].计算机应用研究,2010,27(6):2075-2077.
[25]肖红,尚福华.基于趋势转折点的时间序列模式表示[J].科学技术与工程,2010(13):3254-3257.
[26] N.Manmoulist,S.Yutao.Fast and Exact Warping of Time Series Using Adaptive Segmental Approximations[J].Machine Learning,2005,2(58):231-267.
[27]贾澎涛,林华,何华灿.时间序列的自适应误差约束分段线性化表示[J].计算机工程与应用,2008,44(5):10-14.
[28]肖辉,马海兵,龚薇.基于时态边缘算子的时间序列分段线性表示[J].计算机工程与应用,2008,44(19):156-159.
[29]詹艳艳,徐荣聪,陈晓云.基于斜率提取边缘点的时间序列分段线性表示方法[J].计算机科学,2006,33(11):139-142.
[30] Berkhin and Pavel.Survey of clustering data mining techniques[EB/OL]. http://citeseer.nj.nec.com/berkhin02survey.html,2002.
[31]韩家炜.数据挖掘:概念与技术[M].北京:机械工业出版社,2006.
[32] D.Genopulos,G.Das.Time Series similarity measures[C].KDD Tutorial of the 6th ACM SIGKDD international conference on knowledge discovery and data mining,2009.
[33] H.Ferhatosmanglu,E.Tuncel,D.Agrawal,et al.Approximate nearest neighbor searching in multimedia databases[C] . Proc of 17th IEEE International Conference on Data Engineering,Heidelberg:IEEE Computer Society Press,2001:503-511.
[34] W.Loh,S.Km,K.WHang.Index interpolation an approach to subsequence matching supporting normalization transform in time-series databases[C].Proc of the 9th ACM CIKN International Conference on Information and Knowledge Management,McLean ACM Press,2000:480-487.
[35] Y.Wu,D.Agrawal,E.Abbadia.A comparison of DFT and DWT based similarity search in time series databases[C].Proc of the 9th ACM CIKN International Conference on Information and Knowledge Management,McLean ACM Press,2000:488-495.
[36] K.Shm,R.Srikang,R.Agrawal.High-dimensional similarity joins[J].IEEE Trans on Knowledge and Data Engineering,2002,14(1):156-171.
[37] S.Km,S.Park,W.Chu.An index based approach for similarity search supporting time warping in large sequence databased[C].Proc of the 17th IEEE International Conference on Data Engineering,Heidelberg:IEEE Computer Society Press,2001:607-614.
[38] H.Keogh.Exact indexing of dynamic time warping[C].Proc of the 28th International Conference on Very Large Databases,HongKong Morgan Kaufmann,2002:406-417.
[39] N.D.Sidiropoulos,R.Bros.Mathematical Programming Algorithms for Regression-based Non-linear Filtering in N-dimensional real space [J] . IEEE Transactions on Signal Processing,1999,47:771-782.
[40] D.Q.Goldin,P.C.Kanellalcis.On similarity queries for time series data:constraint specification and implementation[C].Proceedings of the First International Conference on Principles and Practice of Constraint Programming,London:Springer-Verlag,1995:137-153.
[41] R.Agrawal,K.Lin,H.S.Sawhney,K.Shim.Fast similarity search inthe presence ofnoise, scaling, and translation in time-series database[C].Proceedings of the 21st International Conference on Very Large Data Bases,1995:490-501.
[42] D.J.Berndt and J.Clifford.Finding Patterns in Time Series:A Dynamic Programming Approach[M].Advances in knowledge discovery and data mining,AAAI/MIT Press,1996:229-248.
[43] E . Keogh , M . Pazzani . Scaling up Dynamic Time Warping for Data mining Applications[C].Proc of the 6th ACM-SIGKDD International Conference on Knowledge Discovery and Data Mining,NewYork:ACM Press,2000:285-289.
[44] E.Keogh,M.Pazzani.Scaling up Dynamic Time Warping to Massive Datasets[C].Proc of the 3rd European Conference on Principles and Practic of Knowledge Discovery in Databases,London:Springer-Verlag,1999:1-11.
[45]吴承祯,洪伟.长苞铁杉种群个体年龄与胸径的多维时间序列模型研究[J].植物生态学报,2002,26(4):403-407.
[46]贾瑞.多维时间序列数据挖掘技术研究[D].南京航空航天大学,计算机软件与理论,2008.
[47]黄河,郑征.基于形状特征k-d树的多维时间序列相似搜索[J].软件学报,2006,17(10):2048-2055.
[48] W.R.Foster.Neural network forecasting of short,noisy time series[J].Computers and Chemical Engineering,1992,16(4):293-297.
[49] Kaastra,Iebeling.Designing a neural network for forecasting financial and economic time series[J].Neurocomputing,1996,10(3):215-236.
[50]马千里,郑启伦,彭宏.基于模糊边界模块化神经网络的混沌时间序列预测[J].物理学报,2009,58(3):1410-1420.
[51]刘涵,刘丁,李琦.基于支持向量机的混沌时间序列非线性预测[J].系统工程理论与实践,2005,25(9):94-99.
[52]许传华,任青文,房定旺.基于神经网络的混沌时间序列预测[J].水文地质工程地质,2003,30(1):30-32.
[53]李明,税爱社,宋政辉.基于神经网络集成的传感器故障诊断研究[J].后勤工程学院学报,2010,26(3):71-76.
[54]谭琦,杨沛.基于变窗口神经网络集成的时间序列预测[J].计算机工程,2009,35(1):176-177.
[55]苏蕾,郑应文.基于神经网络自适应集成的短期负荷预测[J].微计算机信息,2008,21.
[56] J.Lin,E.Keogh,S.Lonardi,B.Chiu.A symbolic representation of time series with implications for streaming algorithms[C].Proceedings of the 8th ACM SIGMOD Workshop on Research issues in data mining and knowledge discovery,2003:2-11.
[57] F.Gullo,G.Ponti,A.Ragarelli,S.Greco.A time series representation model for accurate and fast similarity detection[J].Pattern Recognition,2009,42(11):2998-3014.
[58] K.P.Chan,A.W.Fu.Efficient time series matching by wavelets[C].Proceedings of the 15th IEEE International Conference on Data Engineering,Sydney,Australia,,1999:126-133.
[59] Y.L.Wu,D.Agrawal,A.E.Abbadi.A comparison of DFT and DWT based similarity search in time-series databases[C].Proceedings of the Ninth International Conference on Information and Knowledge Management,Washington,2000:488-495.
[60] J.S.Richman,J.Randall Moorman.Physiological time series annlysis using approximate entropy and sample entropy[J],Am.J.Physiol,Heart Physio,2000:2039-2049.
[61] G.Anjan,J.Ruoming,G.Agrawal.Fast and Exact Out-of-Core K-Means Clustering[C].Proceedings of the Fourth IEEE International Conference on DataMining,ICDM,2004.
[62]陈志强,刘钊,张建辉.聚类分析中PAM算法的分析与实现[J].计算机与现代化,2003,9:1-3.
[63] Sudipto Guha,Rajeev Rastogi and Kyuseok Shim.ROCK:A Robust Clustering Algorithm for Categorical Attributes[C].In Proceedings of the 15th International Conference on Data Engineering,Sydney,Australia,1999:512-521.
[64] Yue Shihong , Li Ping , Guo Jidong , et a1 . Using Greedy algorithm : DBSCAN revisited[J].Journal ofZhejiang University Science,2004,5(11):1405-1412.
[65] M.Ankerst,M.Breuning,et a1.OPTICS:ordering points to indentify the clustering structure[J],SIGMOD’99,Philadelphia,1999:49-60.
[66] W.Zhang,J.Yang,R.Muntz.STING:A statistical information grid approach to spatial datamining[J].VLDB’97,Athens,Greece,1997:186-195.
[67] R.Agrawal,J.gehrke,D.Gunopulos,et a1.Automatic subspace clustering of highdimensional data for data mining applications[C].In Peoceedings of the ACM SIGMOD Conference on Management of Data,Montreal,Canada,1998.
[68] S.Gholamosein,C.Surojit,Z.Aidong.WaveCluster:a wavelet-based clustering approach for spatial data in very large database[J].The VLDB Journal,2000,8:289-304.
[69] Gautam Das,Dimitrios Gunopulos and Heikki Mannila.Finding Similar Time Series[C].In proceedings of Principles of Data Mining and Knowledge Discovery,London:Springer-Verlag,1997:88-100.
[70] D.Q.Goldin,P.C.Kanellalcis.On similarity queries for time-series data:constraint specification and implementation[C].Proceedings of the FirstInternational Conference on Principles and Practice of ConstraintProgramming,London:Springer-Verlag,1995:137-153.
[71] W.K.Loh,S.W.Kim,and K.Y.Whang.Index Interpolation:An Approach for Subsequence Matching Supporting Normalization Transform in Time-Series Databases[C].Proc of the 9th international conference on Information and knowledge management,New York:ACM Press,2000:480-487.
[72] J.Berndt,J.Clifford.Using Dynamic Time Warping to Find Patterns in Time Series[C].In Proceedings of the KDD Workshop,Seattle,1994:359-370.
[73]魏权龄.数量经济学[M].北京:中国人民大学出版社,1998.
[74]郑煜,孟军.时间序列模型对黑龙江省水稻比较优势的预测[J].东北林业大学学报,2008,10:65-67.
[75]何书元.应用时间序列分析[M].北京:北京大学出版社,2003.
[76]张美英,何杰.时间序列预测模型研究简介[J].江西科学,2009,27(5):697-701.
[77] P.Sollich,A.Krogh.Learning with ensembles:how over-fitting can be useful.In:Touretzky D,Mozer M,Hasselmo M eds.Advances in Neural Information Processing Systems,Cambridge,MA:MIT Press,1996,8:190-196.
[78] LK.Hansen,P.Salamon.Neural network ensembles[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(10):993-1001.
[79] A.Krogh,J.Vedelsby.Neural network ensembles,cross validation and active learning.In:Tesauro 13I Touretzky D,Leen T eds.Advances in Neural Information Processing Systems,Cambridge,MA:MIT Press,1995:231-238.
[80] R.E.Schapire.The strength of weak learn ability[J].Machine Learning,1990,5(2):197-227.
[81] L.Breiman.Bagging predictors[J].Machine Learning,1996,24(2):123-140.
[82] Y.Freund.Boosting a weak learning algorithm by majority[C].Inform. and Comput,1995,121: 256--285.
[83] Y.Freund,R.E.Schapire.Experiments with a New Boosting Algorithm[C].In Proceedingsof the Thirteenth International Conference on Machine Learning,Morgan Kaufmann,1996:148-156.
[84] R.Bryll,R.G.Osuna,F.Quek.Attribute bagging:Improving accuracy of classifier ensembles by using random feature subsets[J].Pattern Recognition,2003,36(6):1291-1302.
[85] W.Yates,D.Partfidge.Use of methodological diversity to improve neural network Generalization[J].Neural Computing and Applications,1996,4(2):114-128.
[86] Z.Shen,F.S.Kong.Optimizing Weights by Genetic Algorithm for Neural Network Ensemble[J].In:Lecture Notes in Computer Science,2004:323-331.
[87]张燕生,白晓颖.一种基于改进的贝叶斯投票算法的服务评估技术[J].计算机科学,2008,35(4):255-258.
[88]吴建鑫,周志华,沈学华,陈兆乾.一种选择性神经网络集成构造方法[J].计算机研究与发展,2000,37(9):1039-1044.
[89] Z.H.ZHOU,J.X.WU.Ensemble neural networks:many could be better than all[J].Artificial Intelligence,2002,137(1):239-263.
[90]朱永红.覆盖聚类算法的应用研究[J].计算机技术与发展,2007,17(1):123-124.
[91] H.Tong.Threshold autoregressive:limit cycles and cyclical data[J].Journal of Royal Statistical Society,1980,42:245-292.
[92]朱慧明,刘智伟.时间序列向量自回归模型的贝叶斯推断理论[J].统计与决策,2004,1:11-12.
[93] R.F.Engle.Autoregressive conditional heteroskedasticity with estimates of the variance of united kingdom inflation[J].Econometrica,1982,50:987-1007.
[94] C.W.J.Granger,R.Joyeux.An introduction to long memory time series models and fractional differencing[J].Journal of Time Series Analysis,1980,1:15-39.
[95] C . W . J . Granger . Longmemory relationship s and the aggregation of dynamics models[J].Journal of Econometrics,1980,14:227-238.
[96] J.R.M.Hosking.Fractional differencing[J].Biometrika,1981,68:165-176.
[97]杨三明,邓春光,刘祥明,石为人.一种基于服务请求的水质预测解析解模型库动态管理方法[P].中国,ZL200910103712.5.
[98]张树京,齐立心.时间序列分析简明教程[M].北京:北方交通大学出版社,2003:102.
[99]贾代平,范洪达.基于信源熵的残差相关性度量方法[J].计算机学报,2005,28(10):1645-1649.
[100]赵国富,曲国庆.聚类分析中CLARA算法的分析与实现[J].山东理工大学学报,2006,20(2):4-10.
[101] R.T.Ng,Jiawei Han.CLARANS:a method for clustering objects for spatial data mining [J].IEEE Transactions on Knowledge and Data Engineering,2002,14(5):1003-1015.
[102]邵峰晶,张斌,于忠清等.多阈值BIRCH聚类算法及其应用[J].计算机工程与应用,2004,40(12):174-176.
[103]冯兴杰,黄亚楼.增量式CURE聚类算法研究[J].小型微型计算机系统,2004,25(10):1847-1849.
[104] W.Hasselbring.Information systems integration[J].Communications of the ACM,2000,43(6):33-38.
[105]王慧斌,王建颖.信息系统集成与融合技术及其应用[M].北京:国防工业出版社,2006:22-28.
[106]吴学玲.基于Web+Services和元数据的信息集成技术研究[D].成都:西南交通大学,2005.
[107]桂浩,陈刚,范吴.XML开发技术教程[M].武汉:武汉大学出版社,2008:252-258.
[108]汪昭,刘欣.信息系统集成方法研究[J].计算机与数字工程,2010,10(38):61-64.
[109] K.Shohei1,Y.Masayuki.Spider algorithm for clustering multivariate time series[J].WSEAS Transactions on Information Science and Applications,2006,3(3):485-492.
[110] B.Chandra,M.Gupta,M.P.Gupta.A multivariate time series clustering approach for crime trends prediction[C].2008 IEEE International Conference on Systems,Man and Cybernetics,2008:892-896.
[111] R.Auguste,A.Ghini,B.Marius. Motion similarity measure between video sequences using multivariate time series modeling[C].2010 International Conference on Machine and Web Intelligence,Publisher:IEEE Computer Society,2010:292-296.
[112] T.Rocha,S.Carvalho,P.Henriques. Wavelet based time series forecast with application to acute hypotensive episodes prediction[C].2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society,Publisher:IEEE Computer Society,2010:2403-2406.
[113] C.Hamzacebi,D.Akay,F.Kutay.Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting[J].Expert Systems with Applications,Publisher:Elsevier Ltd,2008,36(2):3839-3844.
[114] D.Neto,S.G.Paulo,G.P.Gustavo,L.J.Aranildo.Combining Artificial Neural Networkand particle swarm system for time series forecasting[C].Proceedings of the International Joint Conference on Neural Networks,Publisher:Institute of Electrical and Electronics Engineers Inc,2009:2230-2237.
[115] R.Aliev,B.Fazlollahi,A.Rashad,B.Guirimov.Linguistic time series forecasting using fuzzy recurrent neural network[J].Soft Computing,Publisher:Springer Verlag,2008,12(2):183-190.
[116] P.GómezGil,C.Ramírez,M.Juan,H.Pomares.A Neural Network Scheme for Long-Term Forecasting of Chaotic Time Series[J].Neural Processing Letters,2011:1-19.
[117] M.A.Abubakar,B.G.Stewart,S.G.McMeekin.An ensemble neural network for recognizing PD patterns[C] . Proceedings of 45th International Universities' Power Engineering Conference,Publisher:IEEE Computer Society,2010.
[118] W.J.Taylor,R.Buizza.Neural network load forecasting with weather ensemble predictions[C].IEEE Transactions on Power Systems,Publisher:Institute of Electrical and Electronics Engineers Inc,2002,17(3):626-632.
[119] K.Siwek,S.Osowski,R.Szupiluk.Ensemble neural network approach for accurate load forecasting in a power system[J].International Journal of Applied Mathematics and Computer Science,Publisher: University of Zielona Gora,2009,19(2):303-315.
[2] E.Keogh.Data mining and machine learning in time series database[C].Proc of 5th Industrial Conference on Data Mining (ICDM),2005.
[3] HAN Jiawei,M.Kamber.Data mining concepts and techniques[M].New York: Morgan Kaufmann Publishers,2000:7-9.
[4] M.Antunes,L.Oliveira.Temporal Data Mining: an overview[C].KDD 2001 Workshop on Temporal Data Mining,2001.
[5] F.M?rchen.Time Series Knowledge Mining[C].Phd Thesis Philipps University Marburg Germany,G?rich & Weiersh?user Marburg,Germany,2006:180-187.
[6] Yang Qiang,Wu Xindong.10 challenging problems in data mining research[J].Inernational Journal of Information Technology & Decision Making,2006,5(4):597-604.
[7] R.Agrawal, C.Faloutsos,A.Swami. Efficient similarity search in sequence databases[C]. Proc of 4th International Conference on Foundations of Data Organization and Algorithms,London:Springer-Verlag,1993:69-84.
[8] C.Faloutsos,M.Ranganathan,Y.Manolopoules.Fast subsequence matching in time-series database[C].Proc of the ACM SIGMOD International Conference on Management of Data, Minepolist:ACM Press,1994:419-429.
[9]潘定,沈君疑.时态数据挖掘的相似性发现技术[J].软件学报,2007,18(2):246-258.
[10] Lin Weiqiang,MA.Orgun,GL.Williams.An overview of temporal data mining[C].Proc of the 1st Australian Data Mining Workshop,Canberra:University of Technology,2002:83-90.
[11] K.Hoppner.Time series abstraction methods:a survey[C].Proc of Jahrestagung Informatics Workshop on Knowledge Discovery in Databases,Dertmund,2002:777-786.
[12]李爱国.时间序列数据分割与时态模式挖掘研究[D].西安:西安交通大学,2003:2-4.
[13] S.Laxman,PS.Sastry.A Surrey of temporal data mining[J].Sadhama Academy Proceedings in Engineering Science,2006,31(2):173-198.
[14] Wang Changzhou,Wang Xiaoyang.Multilevel filtering for high dimensional nearest neighbor search[C].Proc of ACM SIGMOD workshop on n Research issues in data mining andknowledge discovery,Dallsa ACM Press,2000:37-43.
[15] I.Popivanov,RJ.Miller.Similarity search over time-series data using wavelets[C].Proc of the 18th International Conference on Data Engineering San Jose.IEEE Computer Society Press,2002:212-221.
[16]张海琴,蔡庆生.基于小波变换的时间序列相似模式匹配[J].计算机学报,2003,26(3):373-377.
[17] E.Keogh,Chakrabartik,Pazzanim,et al.Dimensionality reduction for fast similarity search in large time series databases[J].Journal of Knowledge and Information Systems,2001,3(3):263-286.
[18] Gex , P . Smyth . Deformable markov model templates for time series pattern matching[C].Proc of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,Boston ACM Press,2000:81-89.
[19] B.K Yi,C.Faloustsos.Fast Time Sequence Indexing for Arbitrary Lp Norms[C].Proc of the 26th International Conference on Very Large Data Bases,San Francisco:Morgan Kaufmann Publishers Inc,2000:385-394.
[20] E.Keogh,K.Chakrabarti,M.Pazzani,et al.Dimensionality Reeducation for similarity search in Larg Time Series Databases[J].Knowledge and Information Systems,2001,8(3).
[21] CS.Perng,H.Wang,S.Zhang,et al.Landmarks:A new model for similarity-based patternquerying in time series databases[C].In:Data Engineering Proceedings,16th International Conference on,2000:33-42.
[22]李爱国,覃征.在线分割时间序列数据[J].软件学报,2004,15(11):1671-1679.
[23]周大镯,李敏强.基于序列重要点的时间序列分割[J].计算机工程,2008,34(23):14-16.
[24]尚福华,孙达辰.基于时间序列趋势转折点的分段线性表示[J].计算机应用研究,2010,27(6):2075-2077.
[25]肖红,尚福华.基于趋势转折点的时间序列模式表示[J].科学技术与工程,2010(13):3254-3257.
[26] N.Manmoulist,S.Yutao.Fast and Exact Warping of Time Series Using Adaptive Segmental Approximations[J].Machine Learning,2005,2(58):231-267.
[27]贾澎涛,林华,何华灿.时间序列的自适应误差约束分段线性化表示[J].计算机工程与应用,2008,44(5):10-14.
[28]肖辉,马海兵,龚薇.基于时态边缘算子的时间序列分段线性表示[J].计算机工程与应用,2008,44(19):156-159.
[29]詹艳艳,徐荣聪,陈晓云.基于斜率提取边缘点的时间序列分段线性表示方法[J].计算机科学,2006,33(11):139-142.
[30] Berkhin and Pavel.Survey of clustering data mining techniques[EB/OL]. http://citeseer.nj.nec.com/berkhin02survey.html,2002.
[31]韩家炜.数据挖掘:概念与技术[M].北京:机械工业出版社,2006.
[32] D.Genopulos,G.Das.Time Series similarity measures[C].KDD Tutorial of the 6th ACM SIGKDD international conference on knowledge discovery and data mining,2009.
[33] H.Ferhatosmanglu,E.Tuncel,D.Agrawal,et al.Approximate nearest neighbor searching in multimedia databases[C] . Proc of 17th IEEE International Conference on Data Engineering,Heidelberg:IEEE Computer Society Press,2001:503-511.
[34] W.Loh,S.Km,K.WHang.Index interpolation an approach to subsequence matching supporting normalization transform in time-series databases[C].Proc of the 9th ACM CIKN International Conference on Information and Knowledge Management,McLean ACM Press,2000:480-487.
[35] Y.Wu,D.Agrawal,E.Abbadia.A comparison of DFT and DWT based similarity search in time series databases[C].Proc of the 9th ACM CIKN International Conference on Information and Knowledge Management,McLean ACM Press,2000:488-495.
[36] K.Shm,R.Srikang,R.Agrawal.High-dimensional similarity joins[J].IEEE Trans on Knowledge and Data Engineering,2002,14(1):156-171.
[37] S.Km,S.Park,W.Chu.An index based approach for similarity search supporting time warping in large sequence databased[C].Proc of the 17th IEEE International Conference on Data Engineering,Heidelberg:IEEE Computer Society Press,2001:607-614.
[38] H.Keogh.Exact indexing of dynamic time warping[C].Proc of the 28th International Conference on Very Large Databases,HongKong Morgan Kaufmann,2002:406-417.
[39] N.D.Sidiropoulos,R.Bros.Mathematical Programming Algorithms for Regression-based Non-linear Filtering in N-dimensional real space [J] . IEEE Transactions on Signal Processing,1999,47:771-782.
[40] D.Q.Goldin,P.C.Kanellalcis.On similarity queries for time series data:constraint specification and implementation[C].Proceedings of the First International Conference on Principles and Practice of Constraint Programming,London:Springer-Verlag,1995:137-153.
[41] R.Agrawal,K.Lin,H.S.Sawhney,K.Shim.Fast similarity search inthe presence ofnoise, scaling, and translation in time-series database[C].Proceedings of the 21st International Conference on Very Large Data Bases,1995:490-501.
[42] D.J.Berndt and J.Clifford.Finding Patterns in Time Series:A Dynamic Programming Approach[M].Advances in knowledge discovery and data mining,AAAI/MIT Press,1996:229-248.
[43] E . Keogh , M . Pazzani . Scaling up Dynamic Time Warping for Data mining Applications[C].Proc of the 6th ACM-SIGKDD International Conference on Knowledge Discovery and Data Mining,NewYork:ACM Press,2000:285-289.
[44] E.Keogh,M.Pazzani.Scaling up Dynamic Time Warping to Massive Datasets[C].Proc of the 3rd European Conference on Principles and Practic of Knowledge Discovery in Databases,London:Springer-Verlag,1999:1-11.
[45]吴承祯,洪伟.长苞铁杉种群个体年龄与胸径的多维时间序列模型研究[J].植物生态学报,2002,26(4):403-407.
[46]贾瑞.多维时间序列数据挖掘技术研究[D].南京航空航天大学,计算机软件与理论,2008.
[47]黄河,郑征.基于形状特征k-d树的多维时间序列相似搜索[J].软件学报,2006,17(10):2048-2055.
[48] W.R.Foster.Neural network forecasting of short,noisy time series[J].Computers and Chemical Engineering,1992,16(4):293-297.
[49] Kaastra,Iebeling.Designing a neural network for forecasting financial and economic time series[J].Neurocomputing,1996,10(3):215-236.
[50]马千里,郑启伦,彭宏.基于模糊边界模块化神经网络的混沌时间序列预测[J].物理学报,2009,58(3):1410-1420.
[51]刘涵,刘丁,李琦.基于支持向量机的混沌时间序列非线性预测[J].系统工程理论与实践,2005,25(9):94-99.
[52]许传华,任青文,房定旺.基于神经网络的混沌时间序列预测[J].水文地质工程地质,2003,30(1):30-32.
[53]李明,税爱社,宋政辉.基于神经网络集成的传感器故障诊断研究[J].后勤工程学院学报,2010,26(3):71-76.
[54]谭琦,杨沛.基于变窗口神经网络集成的时间序列预测[J].计算机工程,2009,35(1):176-177.
[55]苏蕾,郑应文.基于神经网络自适应集成的短期负荷预测[J].微计算机信息,2008,21.
[56] J.Lin,E.Keogh,S.Lonardi,B.Chiu.A symbolic representation of time series with implications for streaming algorithms[C].Proceedings of the 8th ACM SIGMOD Workshop on Research issues in data mining and knowledge discovery,2003:2-11.
[57] F.Gullo,G.Ponti,A.Ragarelli,S.Greco.A time series representation model for accurate and fast similarity detection[J].Pattern Recognition,2009,42(11):2998-3014.
[58] K.P.Chan,A.W.Fu.Efficient time series matching by wavelets[C].Proceedings of the 15th IEEE International Conference on Data Engineering,Sydney,Australia,,1999:126-133.
[59] Y.L.Wu,D.Agrawal,A.E.Abbadi.A comparison of DFT and DWT based similarity search in time-series databases[C].Proceedings of the Ninth International Conference on Information and Knowledge Management,Washington,2000:488-495.
[60] J.S.Richman,J.Randall Moorman.Physiological time series annlysis using approximate entropy and sample entropy[J],Am.J.Physiol,Heart Physio,2000:2039-2049.
[61] G.Anjan,J.Ruoming,G.Agrawal.Fast and Exact Out-of-Core K-Means Clustering[C].Proceedings of the Fourth IEEE International Conference on DataMining,ICDM,2004.
[62]陈志强,刘钊,张建辉.聚类分析中PAM算法的分析与实现[J].计算机与现代化,2003,9:1-3.
[63] Sudipto Guha,Rajeev Rastogi and Kyuseok Shim.ROCK:A Robust Clustering Algorithm for Categorical Attributes[C].In Proceedings of the 15th International Conference on Data Engineering,Sydney,Australia,1999:512-521.
[64] Yue Shihong , Li Ping , Guo Jidong , et a1 . Using Greedy algorithm : DBSCAN revisited[J].Journal ofZhejiang University Science,2004,5(11):1405-1412.
[65] M.Ankerst,M.Breuning,et a1.OPTICS:ordering points to indentify the clustering structure[J],SIGMOD’99,Philadelphia,1999:49-60.
[66] W.Zhang,J.Yang,R.Muntz.STING:A statistical information grid approach to spatial datamining[J].VLDB’97,Athens,Greece,1997:186-195.
[67] R.Agrawal,J.gehrke,D.Gunopulos,et a1.Automatic subspace clustering of highdimensional data for data mining applications[C].In Peoceedings of the ACM SIGMOD Conference on Management of Data,Montreal,Canada,1998.
[68] S.Gholamosein,C.Surojit,Z.Aidong.WaveCluster:a wavelet-based clustering approach for spatial data in very large database[J].The VLDB Journal,2000,8:289-304.
[69] Gautam Das,Dimitrios Gunopulos and Heikki Mannila.Finding Similar Time Series[C].In proceedings of Principles of Data Mining and Knowledge Discovery,London:Springer-Verlag,1997:88-100.
[70] D.Q.Goldin,P.C.Kanellalcis.On similarity queries for time-series data:constraint specification and implementation[C].Proceedings of the FirstInternational Conference on Principles and Practice of ConstraintProgramming,London:Springer-Verlag,1995:137-153.
[71] W.K.Loh,S.W.Kim,and K.Y.Whang.Index Interpolation:An Approach for Subsequence Matching Supporting Normalization Transform in Time-Series Databases[C].Proc of the 9th international conference on Information and knowledge management,New York:ACM Press,2000:480-487.
[72] J.Berndt,J.Clifford.Using Dynamic Time Warping to Find Patterns in Time Series[C].In Proceedings of the KDD Workshop,Seattle,1994:359-370.
[73]魏权龄.数量经济学[M].北京:中国人民大学出版社,1998.
[74]郑煜,孟军.时间序列模型对黑龙江省水稻比较优势的预测[J].东北林业大学学报,2008,10:65-67.
[75]何书元.应用时间序列分析[M].北京:北京大学出版社,2003.
[76]张美英,何杰.时间序列预测模型研究简介[J].江西科学,2009,27(5):697-701.
[77] P.Sollich,A.Krogh.Learning with ensembles:how over-fitting can be useful.In:Touretzky D,Mozer M,Hasselmo M eds.Advances in Neural Information Processing Systems,Cambridge,MA:MIT Press,1996,8:190-196.
[78] LK.Hansen,P.Salamon.Neural network ensembles[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(10):993-1001.
[79] A.Krogh,J.Vedelsby.Neural network ensembles,cross validation and active learning.In:Tesauro 13I Touretzky D,Leen T eds.Advances in Neural Information Processing Systems,Cambridge,MA:MIT Press,1995:231-238.
[80] R.E.Schapire.The strength of weak learn ability[J].Machine Learning,1990,5(2):197-227.
[81] L.Breiman.Bagging predictors[J].Machine Learning,1996,24(2):123-140.
[82] Y.Freund.Boosting a weak learning algorithm by majority[C].Inform. and Comput,1995,121: 256--285.
[83] Y.Freund,R.E.Schapire.Experiments with a New Boosting Algorithm[C].In Proceedingsof the Thirteenth International Conference on Machine Learning,Morgan Kaufmann,1996:148-156.
[84] R.Bryll,R.G.Osuna,F.Quek.Attribute bagging:Improving accuracy of classifier ensembles by using random feature subsets[J].Pattern Recognition,2003,36(6):1291-1302.
[85] W.Yates,D.Partfidge.Use of methodological diversity to improve neural network Generalization[J].Neural Computing and Applications,1996,4(2):114-128.
[86] Z.Shen,F.S.Kong.Optimizing Weights by Genetic Algorithm for Neural Network Ensemble[J].In:Lecture Notes in Computer Science,2004:323-331.
[87]张燕生,白晓颖.一种基于改进的贝叶斯投票算法的服务评估技术[J].计算机科学,2008,35(4):255-258.
[88]吴建鑫,周志华,沈学华,陈兆乾.一种选择性神经网络集成构造方法[J].计算机研究与发展,2000,37(9):1039-1044.
[89] Z.H.ZHOU,J.X.WU.Ensemble neural networks:many could be better than all[J].Artificial Intelligence,2002,137(1):239-263.
[90]朱永红.覆盖聚类算法的应用研究[J].计算机技术与发展,2007,17(1):123-124.
[91] H.Tong.Threshold autoregressive:limit cycles and cyclical data[J].Journal of Royal Statistical Society,1980,42:245-292.
[92]朱慧明,刘智伟.时间序列向量自回归模型的贝叶斯推断理论[J].统计与决策,2004,1:11-12.
[93] R.F.Engle.Autoregressive conditional heteroskedasticity with estimates of the variance of united kingdom inflation[J].Econometrica,1982,50:987-1007.
[94] C.W.J.Granger,R.Joyeux.An introduction to long memory time series models and fractional differencing[J].Journal of Time Series Analysis,1980,1:15-39.
[95] C . W . J . Granger . Longmemory relationship s and the aggregation of dynamics models[J].Journal of Econometrics,1980,14:227-238.
[96] J.R.M.Hosking.Fractional differencing[J].Biometrika,1981,68:165-176.
[97]杨三明,邓春光,刘祥明,石为人.一种基于服务请求的水质预测解析解模型库动态管理方法[P].中国,ZL200910103712.5.
[98]张树京,齐立心.时间序列分析简明教程[M].北京:北方交通大学出版社,2003:102.
[99]贾代平,范洪达.基于信源熵的残差相关性度量方法[J].计算机学报,2005,28(10):1645-1649.
[100]赵国富,曲国庆.聚类分析中CLARA算法的分析与实现[J].山东理工大学学报,2006,20(2):4-10.
[101] R.T.Ng,Jiawei Han.CLARANS:a method for clustering objects for spatial data mining [J].IEEE Transactions on Knowledge and Data Engineering,2002,14(5):1003-1015.
[102]邵峰晶,张斌,于忠清等.多阈值BIRCH聚类算法及其应用[J].计算机工程与应用,2004,40(12):174-176.
[103]冯兴杰,黄亚楼.增量式CURE聚类算法研究[J].小型微型计算机系统,2004,25(10):1847-1849.
[104] W.Hasselbring.Information systems integration[J].Communications of the ACM,2000,43(6):33-38.
[105]王慧斌,王建颖.信息系统集成与融合技术及其应用[M].北京:国防工业出版社,2006:22-28.
[106]吴学玲.基于Web+Services和元数据的信息集成技术研究[D].成都:西南交通大学,2005.
[107]桂浩,陈刚,范吴.XML开发技术教程[M].武汉:武汉大学出版社,2008:252-258.
[108]汪昭,刘欣.信息系统集成方法研究[J].计算机与数字工程,2010,10(38):61-64.
[109] K.Shohei1,Y.Masayuki.Spider algorithm for clustering multivariate time series[J].WSEAS Transactions on Information Science and Applications,2006,3(3):485-492.
[110] B.Chandra,M.Gupta,M.P.Gupta.A multivariate time series clustering approach for crime trends prediction[C].2008 IEEE International Conference on Systems,Man and Cybernetics,2008:892-896.
[111] R.Auguste,A.Ghini,B.Marius. Motion similarity measure between video sequences using multivariate time series modeling[C].2010 International Conference on Machine and Web Intelligence,Publisher:IEEE Computer Society,2010:292-296.
[112] T.Rocha,S.Carvalho,P.Henriques. Wavelet based time series forecast with application to acute hypotensive episodes prediction[C].2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society,Publisher:IEEE Computer Society,2010:2403-2406.
[113] C.Hamzacebi,D.Akay,F.Kutay.Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting[J].Expert Systems with Applications,Publisher:Elsevier Ltd,2008,36(2):3839-3844.
[114] D.Neto,S.G.Paulo,G.P.Gustavo,L.J.Aranildo.Combining Artificial Neural Networkand particle swarm system for time series forecasting[C].Proceedings of the International Joint Conference on Neural Networks,Publisher:Institute of Electrical and Electronics Engineers Inc,2009:2230-2237.
[115] R.Aliev,B.Fazlollahi,A.Rashad,B.Guirimov.Linguistic time series forecasting using fuzzy recurrent neural network[J].Soft Computing,Publisher:Springer Verlag,2008,12(2):183-190.
[116] P.GómezGil,C.Ramírez,M.Juan,H.Pomares.A Neural Network Scheme for Long-Term Forecasting of Chaotic Time Series[J].Neural Processing Letters,2011:1-19.
[117] M.A.Abubakar,B.G.Stewart,S.G.McMeekin.An ensemble neural network for recognizing PD patterns[C] . Proceedings of 45th International Universities' Power Engineering Conference,Publisher:IEEE Computer Society,2010.
[118] W.J.Taylor,R.Buizza.Neural network load forecasting with weather ensemble predictions[C].IEEE Transactions on Power Systems,Publisher:Institute of Electrical and Electronics Engineers Inc,2002,17(3):626-632.
[119] K.Siwek,S.Osowski,R.Szupiluk.Ensemble neural network approach for accurate load forecasting in a power system[J].International Journal of Applied Mathematics and Computer Science,Publisher: University of Zielona Gora,2009,19(2):303-315.
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