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.
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.
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 paper gives the algorithm for mining time-series pattern or rules and illustrates the...
并讨论了“检测”三次方时间序列模式的一些特性，揭示了当代气候的演化和突变的一些数学机制。
Finally, some mathematical mechanisms about climatic evolution and climatic catastrophe are discovered by studying the features of a censcored cubic time series model.
研究了利用GM(1,1)模型发现时间序列模式的方法，用GM(1,1)模型可以从时间序列中寻找变化规律，预测将来的发展趋势。
Investigates a method of time series model with grey system model. Rules in time series can be found by GM (1, 1) model and the trend of the time series can be forecasted.
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.
针对其时间序列的特点，研究了ARIMA的不同模式，提出了面向特定市场的ARIMA模型，及其预测和估计方法。
Aim at the trait of time series, investigate the different ARIMA patterns, put forward the ARIMA model and forecast and estimate aim at special market.
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。
It involves statistics, profiling and pattern recognition, behavioral analysis, time series analysis, predictive modeling, visualization, cause-and-effect studies and more.
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.
本文首先略述用自回归模式去拟合平稳时间序列的各种方法；
The methods for fitting the autoregressive model to the stationary time series are briefly reviewed.
多平稳时间序列，“格兰其”成员因果律测试和自回归模式给的矢量。
For multiple stationary time series Granger causality tests and vector autoregressive models are presented.
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.
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。
SVM applications, such as pattern recognition, function approaching, time series prediction, fault prediction and recognition, information security, power system and power electronics, are described.
通过寻找过程相似的有效指标，建立了一个适合于时间序列分析的相似预报模式。
After defining a effective index for process similarity, an analogue model, which is suitable for doing similarity forecast for a time series, was established.
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.
文中提出将传感器阵列时间序列信号直接输入到一种具有丰富动力学特性的嗅觉神经网络中进行模式分类的方法。
This paper presents a novel method directly dealing with time series of the sensors responses based on an olfactory neural network with many dynamic properties.
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。
Use a method of time series piecewise linear representation based on feature points as a way for pattern representation.
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.
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。
This paper imported an algorithm which was based on the pattern representation of time series extract outlier value.
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.
先将非平稳时间序列进行经验模式分解，再对各个分量分别建模，最后将各分量预测结果进行组合。
Empirical mode decomposition is used for pre-processing. Decompose time series, then make models separately and combine all the values.
实验表明该方法能够有效地监测出时间序列中的例外模式。
Patterns with OSoutlier patterns. Experimental results demonstrate that the proposed method can identify outlier pattern efficiently.
将经验模式分解和多层前向网络的交叉覆盖算法相结合，提出一种时间序列相似模式的匹配算法。
This paper proposes an effective time series matching method by combining the empirical mode decomposition (EMD) with the alternative covering algorithm.
结果表明，时间可预测模式比滑动可预测模式更接近胶辽海峡历史地震的时间序列特征。
The conclusion is that the time predictable model is more closer to the time series character history earthquakes than the slip predictable model.
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.
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.
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.
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.
该方法的新颖性在于定义时间序列的递归状态时，不仅考虑局部相空间距离而且考虑局部排序模式分布结构。
The innovation of HRP is that the recurrence is defined not only by local phase space distance, but also by the local order patterns structure of a time series.
这作为全文研究的基础，贯穿于时间序列部分周期模式挖掘和增量挖掘分析的全过程。
They are the foundation of research on Algorithm of Mining Partial Periodic Patterns and they can be used in the whole paper.
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