Research on time series data mining is one of important hot spots of data mining.
目前时间序列的数据挖掘是数据挖掘的重要研究热点之一。
Rough set theory, as an effective tool to deal with vagueness and uncertainty, is effective to the time series data mining.
粗糙集理论作为一种处理模糊和不确定性问题的有效工具，对时间序列的数据挖掘是有效的。
This hybrid model synthesizes the merits of multiple intelligent computation methods and offers a new effective solution of time series data mining.
该混合模型融合多种智能计算方法优点于一体，为时序数据挖掘提供了一种新的实用方法。
A way of time series data mining was put forward based on the exploratory analysis and the mathematics module was founded by way of using linear regression technology.
提出了基于探索性分析的时序数据挖掘方法，采用线性回归技术建立了数学模型。
Among these research fields, time series data mining is a rather complex branch, which is a technique that extracts the most valuable information from large amount of history time series data.
而在这其中时间序列数据挖掘是面向特殊应用数据挖掘领域中比较复杂的一个分支，主要研究从大量时间序列历史数据中挖掘有价值信息的方法和相关技术。
In the final chapter, we mine stock trading data using time series method, find out the model and outliers in the data and, at last, we show the more exact forecasting model and outlier mining method.
第五章利用时间序列的方法对证券交易数据进行了挖掘，找出了数据中的模式和异常，相对传统方法而言，给出了更精确的预测模型和异常挖掘方法。
Based on the project background, an improved outlier data mining algorithm for time series data is given out.
根据课题背景，给出一个针对时序数据的离群数据挖掘算法的改进算法。
As one of the important forms of complex data, time series is a hotspot in data mining area.
作为一类重要的复杂类型数据，时间序列已成为数据挖掘领域的热点研究对象之一。
Mining frequent patterns in transaction databases, time series databases, and many other kinds of databases has been studied popularly in data mining research.
挖掘事务数据库、时间序列数据库中的频繁模式已经成为数据挖掘中很受关注的研究方向。
Data mining are used to analyze the foreign exchange rate time series and acquire the correct, implicated and hidden information, which has practical significance in the financial field.
利用数据挖掘技术分析外汇汇率时间序列，从时间序列中获得正确的、隐含的、潜在的信息对于金融领域研究具有重要的现实意义。
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。
Mining similar sequences and similar trends in time-series databases is a novel and important problem in data mining literature.
时间序列数据库中相似序列与相似趋势的挖掘，是数据挖掘领域的一个较新的重要问题。
As a very common type of the data sets, time series has been one of the focuses of the current data mining research.
时间序列数据在数据库数据中十分普遍，于是对时间序列进行数据挖掘已成为当前研究的焦点之一。
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.
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。
Focusing on the problem of data mining in time-series, did research in transforming time-series to trend sequences and methods of performing data mining in acquired trend sequences.
本文针对时间序列的数据挖掘问题，研究了将时间序列转化为趋势序列，以及趋势序列中的数据挖掘问题。
Based on above analysis, this paper integrates the study of data mining and financial time series.
基于上述原因，本文将数据挖掘和金融时间序列结合在一起进行研究。
While as the particularity of data description, researchers pay much attention to how to apply the traditional data mining technologies to time-series data mining and forecasting.
由于数据描述的特殊性，如何把传统的数据挖掘技术应用于时间序列的挖掘与预测中更加受到国内外学者的广泛关注。
In our daily life, there are various kinds of time series data, and time series prediction becomes one of the important aspects of data Mining and Knowledge Discovery (DMKD).
在日常生活中广泛存在着各种时间序列数据，发现时间序列知识、对时间序列进行预测正成为数据挖掘与知识发现的重要内容。
Other modeling techniques include ANOVA, time series, and data mining.
其他建模技术包括方差分析，时间序列，和数据挖掘。
At the same time, however, the nonlinear and chaotic characteristic of time-series data makes the mining be a difficult issue.
然而，时间序列数据的非线性混沌特点，使得对它的挖掘成为难题。
The tasks of data mining include association rules analysis, time series module, cluster analysis, classification and predication and so on.
数据挖掘的任务有关联分析、时序模式、聚类、分类与预测等。
However, there are a few on multivariate time series mining, since the data structure of multivariate time series is more complex than that of univariate time series.
然而多元时间序列的数据结构比一元时间序列更复杂，现有的理论和方法仍不够完善。
The algorithm is applied for similarity mining of the time series data of the electrical loads for a steel plant. The simulation results show the effectiveness of the algorithm.
将该算法用于某钢铁企业的电力负荷时序数据，计算结果表明了算法的有效性。
Mining time series data with association rules is a new field.
采用关联规则挖掘时序数据是较新的研究领域。
As the digitalization technology and database technology advanced recent years, data mining techniques that focus on multi-dimensional time series attracts more and more researchers.
近年来，随着数据库技术以及数字化技术的不断进步，针对高维时间序列的数据挖掘研究引起了越来越多学者广泛的兴趣。
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。
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。
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