嵌入的ts()函数通过向量glarp$livingroom创建一个时间序列对象。
The embedded ts() function creates a time series object out of the vector glarp$livingroom.
在时间、日期和线性序列之中，漩涡不会在同一个向量或相同的天文重力场中存在，你们也因此不会得到任何益处。
In time and date and linear sequence, the vortices would then not exist in the same vector, or in the same specific astronomical gravity fields, and you would not then gain the benefits.
通过时间序列分析建立反映切削状态的数学模型，从动态数据中凝聚信息，构成用于判别的特征向量。
By time series analysis, we build models depicting the cutting tool states, coacervate information from dynamic date and construct feature vectors for discrimination.
一些研究认为，原观测场时间序列的第一特征向量场与时间平均场表现出强相似。
Some studies holded that the first eigenvector field of the time series of original observations fields usually shows a striking similarity to the time averaged field.
混沌和支持向量机理论为研究复杂多变的非线性水文时间序列开辟了新的途径。
Chaos and support vector machine theory has opened up a new route to study complicated and changeable non-linear hydrology time series.
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.
用支持向量回归（SVR）的方法分析和预测时间序列，可解决复杂非线性系统的建模问题。
The Support Vector Regression(SVR)is used for the time series analysis and prediction to resolve the complex nonlinear system modeling problems.
同时由于时间序列的非线性，常规的线性向量自回归模型难以正确描述经济变量之间的因果关系。
At the same time, due to the non-linear condition of time sequence, conventional linear Vector Autoregressive model can hardly characterize the causality among economic variables correctly.
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.
构造了元件故障向量、维修向量、故障频率序列和修复序列，并利用其运算描述了元件的工作、故障、维修和修复过程，从而计算出在事后维修方式和维修时间分布影响下的元件动态可靠性指标：故障频率，有效度。
With them the process of units work, failure, repair and renovation is described, sequentially the dynamic failure and availability of the units can be computed under the influence of the repair time.
提出了客户时间序列的加权处理方法，并应用客户时间序列的统计特征作为聚类特征向量，采用混合式遗传算法对客户聚类，使每一类客户具有相似的时序特征。
A weighted method of customer's time series is proposed and statistical features of time series are adopted for customer clustering, which make each group of customers have similar sequence feature.
把最终的季节时间序列模型转化为状态空间形式，通过卡尔曼滤波实时调整状态向量，实现电梯交通流的在线预测。
It transforms the finial SARIMA model to state space model, adjusts the state vector using Kalman filter, and realizes the on-line forecast.
利用它对签名样本的动态信息时间序列进行校正，可以提高签名特征向量在特征空间上分布的聚拢性，拉开真、伪签名特征向量在特征空间上的距离。
It can effectively increase the compactness of the feature vectors of genuine signatures, and therefore enlarge the distance from the feature vectors of forgery ones.
针对故障率时间序列的非线性与非平稳特性，提出一种基于支持向量经验模态分解(SVEMD)的预测方法。
A prediction method based on support vector empirical mode decomposition (SVEMD) is proposed to deal with the non-linearity and non-stationarity of failure rate data.
通过对时间序列建立向量误差修正模型，运用单位根检验、协整检验、格兰杰因果关系检验、脉冲响应函数等方法精确地度量系统中变量之间相互影响的动态过程。
The article USES VECM, ADF Test, Johansen Test, Granger Causal Relation Test, Impulse Response Function to accurately measure the process that the variables influence each other in the system.
通过对时间序列建立向量误差修正模型，运用单位根检验、协整检验、格兰杰因果关系检验、脉冲响应函数等方法精确地度量系统中变量之间相互影响的动态过程。
The article USES VECM, ADF Test, Johansen Test, Granger Causal Relation Test, Impulse Response Function to accurately measure the process that the variables influence each other in the system.
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