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题名:
经营系统中的时间序列分析
作者: 王颖波
答辩日期: 2003
专业: 计算机软件与理论
授予单位: 中国科学院软件研究所
授予地点: 中国科学院软件研究所
学位: 博士
关键词: 数据挖掘 ; 时间序列 ; 特征提取 ; ARIMA(自回归综合移动平均数) ; 支持向量
摘要: 近年来,数据挖掘引起了信息产业界的极大关注,其主要原因是很多领域中的数据量以极快的速度增长,我们迫切需要将这些数据转换成有用的信息和知识。时间序列的分析是数据挖掘领域中的一个重要组成部分,在现实生活中的很多方面都有广泛的应用。例如:股票分析、心电图分析、交通旅游的客流分析、产品的销售量分析等都可以归类为时间序列分析问题。本文讨论了几种有效的对于具体时间序列的分析方法,分析了每种方法的特点和适用环境,详细介绍了ARIMA模型的原理和实现过程。仅仅有单时间序列的分析还无法满足某些实际系统的需求。应用系统中有几百项或者上千项数据项是很正常的情况;面对这么多项数据,要求管理者对其中所有数据的时间序列分析结果都一一研究是不现实的。因此要对所有数据项进行聚类分析。我们首先抽取时间序列的特征,再根据特征向量对时间序列进行聚类分析。文中提出了根据预测模型系数确定时间序列特征向量的方法。用这种方法,可以将一个含有多个观测点的时间序列特征压缩在只有几位的特征向量中,同时又包含了足够多的序列信,息。我们面临了这样的应用系统背景:对于一个企业,大量的历史数据无法提供明确有效的信息以帮助企业领导进行决策,有用的信息湮没在繁杂的数据当中。根据上面所提到的方法,我们设计并完成该企业的辅助决策系统。在这个分析系统中,提供对于每项数据的时一间序列分析。以及对所有数据的聚类分析功能。
英文摘要: Data Mining attracts more attention in both academy and industry in recently year. The data size of many fields are increasing tremendously whereas useful information extracted from this data is necessary. Time Series analysis is one important part of data mining, which has it wide application in realities. For instance, the analysis of stock, the analysis of cardiogram, sales of some product are examples of time series analysis. In this thesis, some effective methods for time series analysis are discussed. The properties and limitations are introduced, especially the theory and implementation of ARMA model. Only the analysis result of time series could not meet the requirement of some practical systems. There are usually hundreds of or thousands of data item in application systems. It is impossible for managers to analyze all the data, Therefore a clustering algorithm for the data items is desirable. Features of time series are first extracted and then clustering algorithms are performed according to the features. In this paper, we proposed the method for feature selection according to specific predicting models. Time series that contains one or more observation points could be represented using features of low dimension while keep most of the information at the same time. We applied our algorithm in a practical system: there are lots of historical data that are necessary to be processed to provide decision support. However, useful information is usually hidden in complicated data. According to the method we proposed, we built up a decision support system. In this system, analysis and clustering of each time series data are provided with some decision recommendation.
语种: 中文
内容类型: 学位论文
URI标识: http://ir.iscas.ac.cn/handle/311060/5826
Appears in Collections:中科院软件所

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Recommended Citation:
王颖波. 经营系统中的时间序列分析[D]. 中国科学院软件研究所. 中国科学院软件研究所. 2003-01-01.
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