1) 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
2) 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
3) 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。
4) multi-spot time series
多点时间序列
1.
Regarded multi-spot road traffic state as research object, a method of road traffic state multi-spot time series forecasting based on state space model was proposed and road traffic state forecasting was extended from single-spot forecasting to multi-spot forecasting.
以多点的道路交通状态为研究对象,把道路交通状态单点预测向多点同时预测扩展,提出了基于状态空间模型的道路交通状态多点时间序列预测方法。
5) multi-correlation of time series
时间序列多相关
6) multi-dimensional time series
多维时间序列
1.
Based on multi-dimensional time series approach,this paper proposes the function relationships among the future daily load,the influences of historic daily load inertia action and meteorologic cumulated effect.
以多维时间序列分析方法为基础,解决了未来日负荷与历史日负荷惯性变化的影响以及气象累计效应的影响显性函数关系,从而为负荷预测人员掌握未来负荷与历史负荷、历史气象要素与当日气象条件之间的规律提供了量化的分析基础。
2.
Especially, research on multi-dimensional time series has become a frontier topic.
时间序列数据挖掘是数据挖掘研究的一个重要组成部分,是计算机技术研究的热点之一,多维时间序列数据挖掘更是一个比较前沿的研究课题。
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