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.
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.
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.
另外，本文用模糊集理论对时间序列数据挖掘过程中的不确定性进行了处理，提出了一种模糊时序数据挖掘的框架。
Moreover, fuzzy sets theory is adopted in the dissertation to deal with the uncertainty of the mining process and a new fuzzy frame of TSDM is given then.
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。
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.
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。
Rough set theory, as an effective tool to deal with vagueness and uncertainty, is effective to the time series data mining.
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。
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.
时间序列信息系统中的决策问题的关键是有效地挖掘历史数据中包含的时序信息。
Furthermore, the key problem of decision making in time series information system is how to effectively mine the time order information in history data.
该文针对网管告警数据库中时间序列存在的连续性、不确定性和模糊性问题，提出了一种基于时态关联规则挖掘告警库的新方法。
For the problems of continuity, uncertainty and fuzziness in the time-series of the network management alarm database, this pa-per puts forward a new mining method based on time-series rules.
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。
Based on above analysis, this paper integrates the study of data mining and financial time series.
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。
As one of the important forms of complex data, time series is a hotspot in data mining area.
其他建模技术包括方差分析，时间序列，和数据挖掘。
Other modeling techniques include ANOVA, time series, and data mining.
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。
Mining frequent patterns in transaction databases, time series databases, and many other kinds of databases has been studied popularly in data mining research.
时间序列模式、分类规则和关联规则挖掘是当前数据挖掘研究中一个热点。
It is a hotspot that the data mining of time serial model, classify rule, association rule in the data mining study currently.
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。
Mining similar sequences and similar trends in time-series databases is a novel and important problem in data mining literature.
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。
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.
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。
At the same time, however, the nonlinear and chaotic characteristic of time-series data makes the mining be a difficult issue.
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。
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.
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。
As a very common type of the data sets, time series has been one of the focuses of the current data mining research.
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。
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.
为了提高风电场风速短期预测的精度，提出了将经验模式分解与数据挖掘方法相结合对风速时间序列进行建模预测。
In order to improve the forecast precision, a forecasting method based on empirical mode decomposition (EMD) and data mining method is proposed.
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。
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).
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。
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.
本文通过时间震级序列数据挖掘方法对地震预报展开了一系列的研究。
Using data mining techniques can be more systematic, in-depth, comprehensive, detailed research on earthquake prediction analysis play a role in promoting.
本文通过时间震级序列数据挖掘方法对地震预报展开了一系列的研究。
Using data mining techniques can be more systematic, in-depth, comprehensive, detailed research on earthquake prediction analysis play a role in promoting.
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