水文时间序列相似性查找模型。
将时序数据有效地映射到特征空间是时间序列相似性搜索的一个关键问题。
Mapping the raw time series data to a modality space effectively is a critical problem in time series similarity search.
时间序列相似性模式搜索是营销时间序列数据仓库中知识发现领域的一个研究热点。
The similarity pattern query about time series is one of the research hotspots in knowledge discovering in the time series database.
水文时间序列相似性查询可用于雨洪过程预测、环境演变分析、水文过程规律分析等方面。
Hydrological time series similarity search can be used for rainfall and flood forecasting, the analysis of environment evolvement and hydrological process, etc.
论文在深入研究和比较各种方法的基础上，探索适合水文数据特点的时间序列相似性搜索的方法。
Based on the research and comparison of different methods, this paper explored the similarity search method of time series which is adaptive to the characteristics of hydrological data.
目前，国内外学者和研究人员采用不同的方法围绕时间序列相似性的研究已取得了一定的成果，并在股票等领域有了一定的应用。
Many different methods and techniques about time series similarity search have been proposed and successful applications have been made in some fields, such as stock analysis.
它们的相似性依赖于原观测场时间序列在相空间中的结构。
Their similarity depends on the structure in the phase space of the time series of original observations fields.
针对多元时间序列的相似性查询问题，给出参数重要度的定义，提出一种基于参数重要度的候选集查询方法。
Aiming at the problem of multivariate time series similarity search, this paper presents the definition of parameter importance degree and puts forward a candidate sets obtaining method based on it.
剖析了时间序列的相似性度量及其特点。
The time series similarity measure and its characteristics were studied.
金融高频数据是一种不等间隔的时间序列，现有的相似性查找技术对高频数据的处理效果不佳。
The existing methods of similarity search are not suitable for high frequency financial data, which is a kind of non-interval time series.
利用自相关函数对恒生指数日收盘价时间序列中的自相似性进行了初步的实证和理论分析。
We do the initial identification and the theoretical analysis with the autocorrelation function towards the time series of HSI daily closing quotation.
网络业务的统计自相似性主要是指在不同时间尺度上观测到的业务流量序列具有相同的统计特性。
The statistic self-similarity mostly means traffic sequence which is observed in different time scale have the same statistic characteristics.
时间序列存在于社会的各个领域，对于时间序列数据挖掘的研究目前主要集中在相似性搜索和模式挖掘上。
Recently the study on data mining of time series mainly concentrates on both the similarity search in a time series database and the pattern mining from a time series.
目前对于时间序列数据挖掘的研究主要集中在相似性搜索和模式挖掘上。
Opposite to mature part of data mining (such as mining of database association rules and classify rules), mining of time series still falls into a new branch.
相似性度量是金融时间序列挖掘中的一项关键技术，但现有的度量方法不适合分析小规模的金融多元时间序列。
Similarity measure is a key technology of time series mining, whose existing methods are not available for the analysis of small-scale multivariate time series.
该系统首先对时间序列进行适当的处理，然后进行相似性搜索，分析未来的短时间的走势是否是历史上的重现。
First, the time series are processed properly and the similarity search is preformed and then analyse whether the short-term trend is the resurgence of history.
对时间序列的趋势性和相似性进行分析，研究了序列模式的挖掘算法等。
We also research the time series and series patterns mining, analysis tendencies and similarity of the time series, study the mining algorithms of the time series.
本文主要研究了时间序列数据挖掘方法中的序列模式和相似性搜索。
This paper analyses all kinds of algorithms used on sequential pattern mining and discusses traditional similarity search techniques.
本文主要研究了时间序列数据挖掘方法中的序列模式和相似性搜索。
This paper analyses all kinds of algorithms used on sequential pattern mining and discusses traditional similarity search techniques.
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