时间序列分析

中国大陆现今地壳形变与GPS坐标时间序列分析

Present-day Crustal Deformation in China and GPS-derived Coordinate Time Series Analysis

基于时间序列分析的IP语音收入预测

IP Sound Revenue Forecast Based on Time Series Analysis

基于客流n日均量的地铁客流量的时间序列分析

Time-series Analysis for Metro Passenger Flow Based on N Days Average Volume

GPS载波相位测量数据的时间序列分析建模研究

Study on modeling of time series analysis for GPS carrier phase measurement data

基于BP神经网络与时间序列分析的柴油机故障诊断

Diesel engine fault diagnosis based on BP neural network and time series analysis

基于时间序列分析算法及Struts的Web管理网分析

A Web telecommunication network forecast analysis system based on Struts and time sequence analysis algorithm

具体用到的分析工具有SWOT分析、时间序列分析方法等等。

The analyzing tools include SWOT Analysis , Time Series Analysis , etc.

2005年南京市PM（10）浓度时间序列分析

Analysis of PM_ ( 10 ) concentration using time series method in 2005 in Nanjing

基于时间序列分析的Kalman滤波方法在MEMS陀螺仪随机漂移误差补偿中的应用研究

Research on the Application of the Time-Serial Analysis Based Kalman Filter in MEMS Gyroscope Random Drift Compensation

刺槐蜂蜜日产量的时间序列分析AR（1）模型相关系数？

Robinia Robinia honey is estimated by time series analysis AR ( 1 ) model , related factors ?

传统的线性协整和ECM在现代时间序列分析中得到广泛应用。

Traditional linear cointegration theory and ECM can be used widely in time series analysis .

SA信号时间序列分析

SA signal time series analysis

然后应用VISUALBASIC（VB）编程语言和数据统计分析软件SPSS软件对石家庄地区地下水动态作了时间序列分析。

Then we did the time series analysis of groundwater dynamic of Shijiazhuang area with Visual Basic ( VB ) programming language and SPSS software .

HHT方法在海水温度时间序列分析中的应用

Analysis of sea water temperature time series using HHT method

用MODIS植被指数的时间序列分析提取荒漠化敏感区域的方法

Method of Abstracting the Desertification Sensitive Areas through Analysis of the Time Series of MODIS Vegetation Indices

用时间序列分析方法对镗削加工误差进行了分析和建模，建立了相应的AR误差模型。

Analysis and modeling are made for the boring error by using time sequential analysis way , and corresponding AR error model is established .

主要阐述了如何利用时间序列分析法中的复杂度法对PCM混沌编码的复杂度进行分析。

Analysis of complexity of PCM chaotic code by using measure of complexity in time sequence analysis is presented .

奇异值分解（SVD）及其在时间序列分析中的应用&算法与问题研究

Singular value decomposition ( svd ) and its applications in time series analysis & algorithms and problems study

应用时间序列分析原理和方法对柔性制造系统（FMS）加工尺寸数据序列进行在线建模。

On-line modeling and forecasting for machining dimension of FMS is studied and carried out by applying time series analysis theory .

时间序列分析主要采用了自回归模型AR（n）：在灰色预测分析中采用了工程中最常用的GM（1，1）模型。

Least square method is adopted in regression analysis , AR ( n ) model in time series analysis and GM ( 1,1 ) model in grey system prediction .

为了检验时间序列分析模型的有效性，使用了信息准则AIC（AnInformationCriterion）函数。

AIC ( An Information Criterion ) function is used to check its validity .

方法运用SPSS10.0统计软件、EXCEL分析软件，作字2检验和时间序列分析t检验。

Methods Statistical analysis was conducted using SPSS software package version 10.0 for Windows and Microsoft Excel to perform x ~ 2 test and time sequence test .

本文主要研究Copula理论及其在多变量金融时间序列分析上的应用。

In this dissertation , copula theory and its applications in multivariate financial time series analysis are studied intensively .

本文根据切削过程中一些参数的变化规律，从理论上首次提出了一种新的时间序列分析模型，即常系数固定价ARC（2）模型。

According to the change pattern of some parameters in metal cutting processes , this paper proposes for the first time a new time series analysis model-Autoregressive Constant Model ARC ( 2 ) .

鉴于干散货运价指数的非线性特征，本文提出了结合混沌时间序列分析的相空间重构和支持向量机（SVM）的混合预测模型，探讨并阐述了混合模型的预测原理及建模思路。

Secondly , this paper discusses the modeling idea and develops a hybrid forecasting model based on Support Vector Machine ( SVM ) and chaotic time series theory .

基于采用ARIMA（p，d，0）模型结构的时间序列分析方法，提出一种短时交通流实时自适应预测算法。

Based on time series analysis method adopting ARIMA ( p , d , 0 ) model , a kind of real-time adaptive forecasting method for short-term traffic flow was presented .

VBR视频存在天然的长程相关和自相似结构，传统的时间序列分析无法对其做出准确的建模。

As the existence of long-range dependence and self-similarity in VBR sequence , traditional time series analysis may not be useful .

该文采用时间序列分析的方法，对现场采集的汽包水位噪声信号进行了分析，采用Burg方法建立了相应的水位噪声信号AR模型；滇池的水位

To solve such problem , by using time series analysis , a noise model is provided in this paper . THE WATER LEVEL IN DIANCHI LAKE

此外，采用时间序列分析方法，建立了IFOG的随机漂移误差模型。

Furthermore , a random drift error model for IFOG is built by the method of time series analysis .

根据城市用水量的影响因素及特点，基于BP神经网络，建立了一种综合多元分析特点和时间序列分析的动态水量预测模型。

Based on BP model of the artificial neural network , according to the factors and features of urban water supply , a dynamic water consumption forecasting model with the characteristics of both regression and time series is developed .