1) Wave-type time series
起伏型时间序列
1.
Wave-type time series variation of the correlation between NDVI and climatic factors.;
植被归一化指数(NDVI)及气候因子相关起伏型时间序列变化分析
2.
Prediction of insect pests by application of analysis for wave-type time series;
起伏型时间序列分析方法在害虫测报上的应用
3.
Using the wave-type time series method, the monthly dynamic changes of the NDVI in China and its representative provinces were simulated in this paper.
应用起伏型时间序列法对中国及其典型省份的NDVI月动态变化进行模拟 。
2) wave-type time series method
起伏型时间序列法
1.
So,wave-type time series method was used to simulate the dynamics of 3 main nutrient elements,and the simulated results were good which indicated that the method could be applied to the study of nutrients dynamics in the litter-fall′s decomposing process.
因此,本研究应用起伏型时间序列法对其动态进行模拟,模拟结果比较理想,说明该方法可应用于凋落物分解过程养分动态的研究。
3) time series model
时间序列模型
1.
Association rules mining in stock time series model;
股票时间序列模型的关联规则挖掘
2.
The establishment and analysis of the time series model of Guangxi s GDP;
广西GDP的时间序列模型的建立与实证分析
3.
General expression for linear and nonlinear time series model and its engineering application;
线性/非线性时间序列模型一般表达式及其工程应用
4) time-series model
时间序列模型
1.
This paper forecasts the gross domestic product of central region and points the development trend by building the time-series model.
通过建立时间序列模型,对中部地区的湖北、湖南、河南、江西和安徽5省GDP进行预测,指出中部地区GDP发展趋势,并通过与全国GDP的比较,找出中部地区GDP的发展差距,并得出相关结论。
2.
We carry on logistics demand forecast by taking time-series model and explain its application to logistics demand forecast by justment of data stationary,stead,standardization,modeling,discernment,making steps,parameter estimate and examination to predict,error and calculation of confidential interval.
本文采用时间序列模型进行物流需求预测,用实例从数据平稳性的判断,平稳化,标准化,建模,经模型的识别、定阶、参数估计和检验,到预测及误差和置信区间的计算,详细地说明了时间序列模型在物流需求预测中是如何应用的。
3.
We establish the combined regression-time-series model for Chinese inflation and compare its forecast results to that of regression model.
回归模型的残差项反映了对被解释变量有影响但未列入解释变量的因素所产生的噪音 ,这部分噪音可由时间序列模型进行拟合 。
5) AR1MA time series model
AR1MA时间序列模型
1.
The Application of AR1MA time series model in income budget of telecom products;
AR1MA时间序列模型在电信产品收入预算中的应用
6) Time Series Models
时间序列模型
1.
Limitations and Improving Methods of Individual Economic Developing Trend Represented by Time Series Models;
时间序列模型描述个体经济发展趋势的局限性及改进方法
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