并给出了两种构造多维时间序列分类的决策树模型算法。
Two algorithms for structuring decision tree model of multi-dimensional time series classification were presented.
文章分析了模型建立的目的和特征，阐述了时间序列分析的定义、分类、特征和应用领域。
This paper analyzed the aim and characteristic of model establish, expounded time series analytic definition, classification, feature and application domain.
另外，结合一类分类方法和相空间重构理论，提出一种时间序列中的异常值检测方法。
In addition, a new method of outlier detection in time series is proposed by combination of phase space theory and one-class classification method.
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.
论文结合相空间重构理论与一类分类方法提出一种时间序列中的异常值检测方法。
A new method of outlier detection in time series is proposed in this paper, which is based on phase space reconstruction theory and one-class classification method.
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.
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.
分析了基于记忆库混沌时间序列预测方法，引入一种改进核函数的支持向量机分类器。
Secondly the prediction technology of chaotic time series is studied based on memory-based predictor.
该方法首先利用HMM生成最佳语音状态序列，然后用函数逼近技术产生对最佳状态序列进行时间规正，最后通过RBF神经网络进行分类识别。
Here, the HMM is employed to produce a best speech state sequence which is warped to a fixed dimension vector and the RBF neural network is used as classifier.
因此本文提出基于属性分类的时间序列预测方案。
Then sample time series was chosen which had the same property with predicting object.
该方法通过对时间序列排序模式进行分类，来实现复杂的概率分布估计，从而直接估计出时间序列的信息量。
The proposed method calculates the probability distribution of time series based on the classification of order patterns to directly estimate the amount of information in time series.
对电极记录的多个神经元锋电位的分类处理是进行锋电位时间序列分析之前所要进行的第一步。
Identification and classification of spike events were the first step in all multiple spike train data analyses.
对电极记录的多个神经元锋电位的分类处理是进行锋电位时间序列分析之前所要进行的第一步。
Identification and classification of spike events were the first step in all multiple spike train data analyses.
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