The paper gives the algorithm for mining time-series pattern or rules and illustrates the...
给出了时间序列模式和规则的挖掘算法，并举例说明该算法是有效和可行的。
The main task of data mining includes correlation analysis, cluster analysis, classification, prediction, time-series pattern, deviation analysis and so on.
数据挖掘的任务主要有关联分析、聚类分析、分类、预测、时序模式和偏差分析等。
In the paper, being directed to the complex model of time-series pattern, drawing idea and giving pseudo code of the algorithm of finding time-series patterns has been presented.
针对时序数据中的复杂模式问题，提出了一种时序模式算法，并描绘了算法的基本思想及给出了算法的简单伪代码。
This paper imported an algorithm which was based on the pattern representation of time series extract outlier value.
提出了一种基于时间序列的模式表示提取时间序列异常值的异常检测算法。
The similarity pattern query about time series is one of the research hotspots in knowledge discovering in the time series database.
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。
Epsilon machine, a new computational mechanics, can discover hidden pattern from the response time series.
机是一种新的计算力学理论，它能从时间序列中发掘系统的隐含模式。
SVM applications, such as pattern recognition, function approaching, time series prediction, fault prediction and recognition, information security, power system and power electronics, are described.
归纳了支持向量机在诸如模式识别、函数逼近、时间序列预测、故障预测和识别、信息安全、电力系统以及电力电子领域中的应用。
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.
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。
By making use of the proximity query method in computational geometry, the whole matching query, pattern query, inverse query and outlier detection in time series are studied.
提出了计算几何应用到时间序列挖掘的方法，实现了时间序列全序列匹配查询、模式查询、反向查询和异常检测，查询效率和准确性都有了比较大的提高。
Use a method of time series piecewise linear representation based on feature points as a way for pattern representation.
使用一种基于特征点的时间序列线性分段方法作为时间序列的模式表示。
The idea is to transform the net audit data into time series database and mine the sequence pattern to extract the user behavior pattern , and then to use behavior pattern in anomaly detection.
其思想是通过将网络审计数据转化为时序数据库，对其进行序列模式挖掘以提炼出用户行为模式，并由此进行异常检测。
It involves statistics, profiling and pattern recognition, behavioral analysis, time series analysis, predictive modeling, visualization, cause-and-effect studies and more.
它涉及到统计，分析和模式识别，行为分析，时间序列分析，预测建模，可视化，因果的研究等等。
According to the change pattern of some parameters in metal cutting processes, this paper proposes for the first time a new time series analysis model-Autoregressive Constant model ARC (2).
本文根据切削过程中一些参数的变化规律，从理论上首次提出了一种新的时间序列分析模型，即常系数固定价ARC(2)模型。
The pattern representation of time series itself has the function of compress data and keeps the basic shape of time series, and it has a certain extent effect of deleting noises.
时间序列的模式表示本身就具有压缩数据、保持时间序列基本形态的功能，并且具有一定的除噪能力。
Some of these maps illustrate the temporal pattern of the subject over a defined period of time while others show finer spatial details through a series of maps drawn in larger scale.
每组地图显示主题在某特定时期内的空间分布情况的变化，或透过一系列高比例的地图显示更多资料细节。
Mining Time Series Frequent Sub-pattern based on Pattern Representation can enormously increase the efficiency and veracity of mining, and get twice the result with half the effort.
在时间序列的模式表示的基础上挖掘其频繁子模式，可以大大提高挖掘的效率和准确性，达到事半功倍的效果。在该算法中，还使用了一定的剪枝策略，使得算法的时间复杂度进一步降低。
Mining Time Series Frequent Sub-pattern based on Pattern Representation can enormously increase the efficiency and veracity of mining, and get twice the result with half the effort.
在时间序列的模式表示的基础上挖掘其频繁子模式，可以大大提高挖掘的效率和准确性，达到事半功倍的效果。在该算法中，还使用了一定的剪枝策略，使得算法的时间复杂度进一步降低。
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