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.
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。
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.
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.
目前，在时间序列分析领域，孤立点的挖掘越来越多的受到重视。
At present, outlier mining has attached a great importance in the field of time series analysis.
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.
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。
Research on time series data mining is one of important hot spots of data mining.
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.
提出一种新的基于符号化表示的时间序列频繁子序列的挖掘算法。
This paper proposes a new algorithm for mining frequent subsequence in time series based on symbolic representation.
提高序列模式挖掘算法效率的关键在于减少发现频繁序列的时间。
To speed up mining sequential patterns, reducing the time cost is very important during discovering sequential frequent sequence.
结合地震预报的领域知识，面向具体的应用，提出了一种改进的基于滑动时间窗口的序贯模式挖掘算法，用来发现广义的地震序列。
An improved sequential pattern mining algorithm is proposed, which is based on sliding time window and can discover general earthquake sequences according to field knowledge.
本文的中缀算法适用于序列挖掘，寻找和时间相关的频繁序列。
The algorithm researches in this paper can be used for sequence mining or searching frequent sequences related to time.
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.
提出了一种针对不同时间序列间关联模式的发现方法，并阐述了以该方法为基础而构建的关联模式挖掘系统的结构。
This paper gives an algorithm for mining association patterns between two different time series, and describes the construction of a mining system.
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.
本文对时间序列模式、分类规则和关联规则挖掘的方法进行了深入的研究。
In this thesis, the thorough study of time serial model, classification rule and association rule is made.
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.
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.
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.
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.
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.
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.
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.
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.
传统序列模式挖掘算法往往忽略了序列模式本身的时间特性，所考查的序列项都是单一事件，无属性约束。
The time trait is often ignored in the course of mining traditional sequential pattern, in which the sequential item is also without attribute constraint.
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.
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.
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.
应用推荐