1) time series method
时间序列方法
1.
The time series method is used to extract the regular part from the residual series received by modeling of observed data.
利用时间序列方法，分析去除确定性部分后的残差序列，提取其中的有规律成分作为预报时的补偿项，形成叠合模型，进行建筑物观测资料的分析。
2.
The monthly central tax revenues forecasting is considered based on time series method.
研究了基于时间序列方法的国税月度收入预测。
2) time series DEA
时间序列DEA方法
4) modern time-series analysis method
现代时间序列分析方法
1.
Using modern time-series analysis method,based on the autoregressive moving average(ARMA)innovation model and white noise estimators,a new two-stage decoupled Wiener filters are presented for descriptor systems with stochastic bias.
应用现代时间序列分析方法 ,基于ARMA新息模型和白噪声估值器 ,提出了一种分离随机偏差两段解耦Wiener滤波新方法 ,同两段解耦Kalman滤波理论相比 ,避免了解Riccati方程 ,实现了完全解耦。
2.
To discrete-linear systems with stained observe noises,based on the ARMA innovation model,using modern time-series analysis method,a new decouple Wiener trace filter method are presented.
对带有色观测噪声的离散线性系统 ,应用现代时间序列分析方法 ,基于ARMA新息模型 ,提出了一种解耦Wiener滤波新方法 ,仿真例子说明了本方法的有效
3.
Using modern time-series analysis method,based on the autoregressive moving av-erage(ARMA)innovation model and white noise estimators,state-input estimation two stage decoupled Wiener filters are presented for stochastic system s with non-determinate control input.
运用现代时间序列分析方法,基于ARMA模型和白噪声估值器,对一类控制输入存在不确知性的随机系统,提出了状态输入估计两段解耦Wiener滤波新算法,仿真例子说明了其有效性。
5) modern time series analysis method
现代时间序列分析方法
1.
By the modern time series analysis method, based on the autoregressive moving average(ARMA) innovation model, the multisensor single channel optimal information fusion Wiener filter is presented under the linear minimum variance fusion criterion for single channel ARMA signals with white observation noise.
应用现代时间序列分析方法,对于带白色观测噪声的单通道ARMA信号,基于ARMA新息模型,提出了多传感器线性最小方差最优信息融合Wiener滤波器,可统一处理滤波、平滑和预报问题。
2.
Using the modern time series analysis method,based on the autoregressive moving average (ARMA)innovation model and white noise estimators,this paper presents two new fixed point Kalman smoothers and two new forward fixed interval Kalman smoothers for linear discrete time invariant stochastic systems.
应用现代时间序列分析方法，基于ＡＲＭＡ新息模型和白噪声估值器，对线性定常离散随机系统提出了两种新的固定点Ｋａｌｍａｎ平滑器和两种新的正向固定区间Ｋａｌｍａｎ平滑器。
3.
By the modern time series analysis method, based on the autoregressive moving average(ARMA)innovation model,and white noise estimation theory,a distributed steady-state Kalman fuser with a three-layer fusion structure is presented,which consists of two weighted fusers and two composite fusers.
对带多传感器的线性离散随机广义系统,用奇异值分解将其化为两个降阶耦合子系统,应用现代时间序列分析方法,基于自回归滑动平均(Autoregressive moving average,ARMA)新息模型和白噪声估计理论,提出了带三层融合结构的分布式稳态Kalman融合器,它由两个加权融合器和两个复合融合器组成。
6) time series method
时间序列法
1.
Time series method is adopted for building the strong wind monitoring and warning system of Qinghai-Tibet Railway to realize the short-term forecast of the wind speed along the line.
青藏铁路大风监测预警系统采用时间序列法实现沿线风速的短时预测。
2.
Summarizing the types and constitution of supply chain costs,the paper uses dynamic programming to solve the cost value in different supply chain links and stages under a fixed cost standard,uses the time series method to forecast and renew the future activity costs and meanwhile uses programming to calculate the optimal costs and dynamic cost trend.
总结了供应链成本的类型与构成,利用动态规划法求解在一定成本标准下供应链不同环节和不同阶段的成本值,利用时间序列法对未来的作业成本进行更新预测。
3.
The reliability is combined with the time series which is corresponding with the reliability,then it is analyzed by time series method;the reliability prediction result can be achieved by the extrapolation of the neural network.
将可靠度与对应的时间序列相结合,采用时间序列法对其进行统计分析,然后应用神经网络向外推测,从而得出电火花线切割机床可靠度的预计结果。
补充资料：离散时间周期序列的离散傅里叶级数表示
(1)
式中χ((n))N为一离散时间周期序列，其周期为N点，即
式中r为任意整数。X((k))N为频域周期序列，其周期亦为N点，即X(k)=X(k+lN)，式中l为任意整数。
从式(1)可导出已知X((k))N求χ((n))N的关系
(2)
式(1)和式(2)称为离散傅里叶级数对。
当离散时间周期序列整体向左移位m时,移位后的序列为χ((n+m))N，如果χ((n))N的离散傅里叶级数(DFS)表示为,则χ((n+m))N的DFS表示为
式中χ((n))N为一离散时间周期序列，其周期为N点，即
式中r为任意整数。X((k))N为频域周期序列，其周期亦为N点，即X(k)=X(k+lN)，式中l为任意整数。
从式(1)可导出已知X((k))N求χ((n))N的关系
(2)
式(1)和式(2)称为离散傅里叶级数对。
当离散时间周期序列整体向左移位m时,移位后的序列为χ((n+m))N，如果χ((n))N的离散傅里叶级数(DFS)表示为,则χ((n+m))N的DFS表示为
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条