1) multiple time-series
多重时间序列
2) multiple time series
多时间序列
1.
A new model for mining multiple time series based on temporal logic
基于时态逻辑的多时间序列挖掘模型
3) time series reconstruction
时间序列重建
1.
A modified time series reconstruction method was presented,which was based on TFN model,Kalman filter and SCE-UA method.
基于TFN模型、Kalman滤波和复合型混合演化(SCE-UA)算法,发展一种新的时间序列重建方法,并将其用于地下水埋深估计。
4) Doubly stochastic time series
双重时间序列
5) multivariate time series
多变量时间序列
1.
Nonlinear prediction of short-term electrical load on multivariate time series;
电力短期负荷的多变量时间序列预测方法研究
2.
Phase space reconstruction of complex systems based on multivariate time series;
多变量时间序列复杂系统的相空间重构
3.
Research of data mining method on multivariate time series;
多变量时间序列模式挖掘的研究
6) multidimensional time series
多维时间序列
1.
Based on support vector regression (SVR) and controlled autoregressive (CAR), we proposed a new non-linear multidimensional time series method named SVR-CAR that can show the dynamic characteristics of sample set as well as the effect of environmental factors.
基于支持向量回归(SVR)并融合带受控项的自回归模型(CAR),建立了一种既反映样本集动态特征又体现环境因子影响的非线性多维时间序列分析预测方法(SVR-CAR)。
2.
Combined support vector regression (SVR) and controlled autoregressive(CAR), a non-linear multidimensional time series approach named SVR-CAR thatbases on structural risk minimization and has high accurate prediction was constructed,which showed the dynamic characteristics of sample set as well as the effect ofenvironmental factors.
基于支持向量回归(support vector regression，SVR)并融合带受控项的自回归模型(controlled autoregressive，CAR)，建立了一种基于结构风险最小原则、既反映样本集动态特征又体现环境因子影响的高精度非线性多维时间序列预测方法(SVR-CAR)，并对5年的小麦赤霉病病穗率和二代玉米螟危害程度进行了一步预测。
3.
To demonstrate the efficiency of our technique,we reconstructed chaotic attractors from multidimensional time series of several existing chaotic systems,such as the Lorenz system,Chen system,Rssler system,Robinovich-fabrikant system and Rssler-hyperchaos system.
在传统的一维时间序列重构技术基础上,提出一种更有效的多维时间序列相空间重构技术。
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