时间序列

词组短语

time series

与时间序列相关的网络例句 [注：此内容来源于网络，仅供参考]

According to the characteristic of statistics, time series is divided into two classes.

时间序列分析按时间序列的统计特性可以分为平稳时间序列和非平稳时间序列两类。

Based on the research work of basic theories including phase space reconstruction,embedding theorem,correlation dimension,local dynamics,Lyapunov exponents,surrogate data etc,based on the research work of general methods such as principal component analysis,correlation dimension GP algorithm,false neighbors method,nonlinear time series prediction,local prediction, adaptive prediction,neural network model,support vector machines regression model, prediction power,nonlinear detection,coarse-graining methodology,conditional entropy and so on,the framework of nonlinear time series analysis are constructed.

在对包括相空间重构、嵌入定理、关联维数、局部动力学、Lyapunov指数、替代数据(来源：A97BC论文网www.abclunwen.com)、等基本理论与其物理意义的研究和讨论基础上；在对包括主分量分析、关联维数GP算法、伪邻近点法、非线性时间序列预测、局域预测、自适应预测、神经网络模型、支持向量回归模型、预测效果、非线性检测、粗粒化方法、条件熵等非线性时间序列一般分析方法的原理和算法研究基础上；构建了新的非线性时间序列分析的理论体系，归纳总结了非线性时间序列分析的基本问题和主要研究方面。2。

The probability model of Extensive Generalized Self-shrinking Generator is constructed. The output time sequences and the output sequences are analyzed: the output time sequences are proved as a homogeneous Markov chain, and the output sequences are independent and identically uniform distributed. The coincidence between the output sequences and some correlation sequences in this generator is given.

然后，建立了泛广义自缩减生成器的概率模型，分析了该生成器的输出时间序列及输出序列的性质，得到了生成器的输出时间序列是一个齐次马氏链，输出序列是独立同均匀分布的随机变量序列，还得到了输出序列与生成器中一些相关序列的符合率。

The testing method comprises the following steps:(1) collecting the electrocardio-signal, the phonocardio-signal and the pulse signal of radial artery;(2) performing analog-to-digital conversion to the three signals to form the signal oscillogram;(3) identifying and extracting the characteristic point of each signal;(4) constructing the time series RR of electrocardio-period, the electromechanical delay time series and the pulse wave propagation time series of the combined variability index;(5) testing the validity of each time series;(6) calculating the heart rate variability, the electromechanical delay variability and the pulse wave propagation time variability; and (7) calculating the combined variability index of angiocarpy AV.

本发明提供了一种心血管系统联合变异性指标的检测方法和装置，检测方法包括以下步骤：(1)采集受检测者的心电、心音、挠动脉脉搏信号；(2)对三路信号进行模拟-数字转换，形成信号波形图；(3)识别和提取三路信号各自的特征点；(4)构造联合变异性指标的心电间期时间序列RR，电机械延迟时间序列和脉搏波传播时间序列；(5)检验各时间序列的有效性；(6)计算心率变异性、电机械延迟变异性和脉搏波传播时间变异性；(7)计算心血管联合变异性指标AV。

In the thesis the theory of time series analysis, several models about time series analysis and the dynamic feature of time series analysis are expatiated in detail; how to use autocorrelation function and partial correlation function to judge the model is analyzed; a kind of better rule to determined rank is determined by comparing several kinds of determined rank; thereby the reverse function can be used to forecast.

本文详细阐明了时间序列的基本思想、几种常见的时间序列模型以及时间序列的动态特征；分析如何利用自相关函数和偏相关函数来对模型进行判定；通过对时间序列的几种定阶准则的比较，确定出一种较好的定阶准则来建立模型，从而利用逆函数法进行预报。

First, the traffic flow time series chaotic feature is extracted by chaos theory. pretreatment for traffic flow time series, and the wavelet neural networks model was build by this. Second, the chaotic mechanism and the chaotic probability is described. Based on chaotic learning algorithm, and the wavelet neural networks fast learning algorithm of traffic flow time series is designed based on chaotic algorithm. Last, a single-step and multi-step prediction of traffic flow chaotic time series is researched by BP neural networks, wavelet neural networks and wavelet neural networks based on chaotic algorithm. The results showed that the wavelet neural networks predictive performance is better than the BP networks and the wavelet neural networks by the simulation results and root-mean-square value.

首先，通过混沌理论提取了交通流量时间序列的混沌特征，并在此基础上建立了小波神经网络交通流量时间序列模型；接着，阐述了混沌学习算法的混沌机理、混沌产生的概率，设计了基于混沌算法的小波神经网络交通流量混沌时间序列快速学习算法；最后利用交通流量混沌时间序列对BP网络、非混沌算法的小波神经网络以及基于混沌算法的小波神经网络进行了单步预测和多步预测，并对预测结果的仿真图和真实值与预测值的方均根进行了比较，结果表明基于混沌学习算法的小波神经网络的预测性能明显优于应用BP网络和非混沌算法的小波神经网络。

To apply ARIMA models the time series needs to be stationary. A stationary time series is one whose statistical properties such as mean, variance and autocorrelation are constant over time. If the initial time series is not stationary it may be that some function of the time series, e.g. taking the differences between successive values, is stationary.

运用ARIMA模型时时间序列必须是固定的，一组固定的时间序列是它的统计特性诸如平均值＼差异和自相关等随着时间的推移而始终不变，如果最初的时间序列不固定这是由它的某些函数引起的，以序列值之间的差额为例，它们是固定的

Some non-stationary time series can be decomposed into several approximate stationary time series with wavelet decomposition.Decomposed time series are forecasted with auto-regression model,to obtain forecasting results of the original time series.

通过小波分解可以将某些非平稳时间序列分解成多层近似意义上的平稳时间序列，然后采用自回归模型对分解后的时间序列进行预测，从而得到原始时间序列的预测值。

The latest time subseries is regarded as the information collector of the time series.

给出了时间序列的变化序列和最近时间子序列的概念，并把最近时间子序列看作是时间序列的信息聚集嚣。

At first, this algorithm obtains rough similar series by clustering the broad moving average; Second, based on this, we can construct the similarity degree of time series trend, by using this similarity degree of time series trend, the users can do riddling secondly. At last, through calculating the distance of the residual time series, accurate similar time series are reached.

该方法首先利用对时间序列广义移动均值的聚类进行相似搜索的粗匹配；然后通过构造时间序列趋势的相似度，利用用户对趋势相似度的要求可以进行第二次筛选；最后对剩余的时间序列进行距离计算，就可以获得最终符合用户需求的相似时间序列。

与时间序列相关的网络解释 [注：此内容来源于网络，仅供参考]

Forecasting Economic Time Series Granger G. & Newbold P：预测经济时间序列

137. Mathematical Economics Takayama A.数理经济 25 | 138. Forecasting Economic Time Series Granger G. &Newbold P.预测经济时间序列 14 | 139. Time Series Analysis Hamilton J.D.时间序列分析 40

Time Series：时间序列

5) 时间序列(Time Series)算法用于分析和预测基于时间的数据. 零售企业的销售额是最常见的使用时间序列算法进行分析和预测的数据. 此算法用于发现多个数据序列所反映出来的模式,以便企业确定不同的元素对所分析序列的影响.

stationary time series：平稳时间序列

对于时间序列数据,经典计量经济学模型只能建立在平稳时间序列(Stationary Time Series)基础之上,很可惜,实际的时间序列很少是平稳的. 由于宏观经济仍然是我国学者进行经验实证研究的主要领域,而宏观时间序列大量是非平稳的,于是出现了大量的错误.

time series analysis：时间序列分析

在法律效果的定量分析文献中,有两种方法都被冠以"时间序列分析(time series analysis)"的名称. 一种就是脱胎于时间趋势回归模型的断续时间序列分析,前文已有介绍. 除法律变量之外,它至少还要引入模拟时间变化趋势的解释变量. 而另一种则建立在N.

Traditional time series analysis：传统时间序列分析

超高频时间序列:Ultra-High-Frequency Time Series | 传统时间序列分析:Traditional time series analysis | 时间序列估算法:estimating methods of time series

Nonlinear time series analysis：非线性时间序列分析

现代时间序列分析方法:modem time-series analysis method | 非线性时间序列分析:Nonlinear time series analysis | 非线性时间序列预测:nonlinear time series forecast

Time series analysis method：时间序列分析法

时间序列估算法:estimating methods of time series | 时间序列分析法:Time series analysis method | 时间序列分析法:time series analytic approach

Chaotic Time Series Analysis：混沌时间序列分析

混沌时间序列预测:Forecasting of chaotic time series | 混沌时间序列分析:Chaotic Time Series Analysis | 时间序列谐波分析法:Harmonic ANalysis of Time Series

Time series data：时间序列数据

第六讲 时间序列模型初步 时间序列模型的例子 有限样本条件下的普通最小二乘估计 大样本条件下的普通最小二乘估计 时间序列的平稳性检验 时间序列模型的例子 计量经济学中的数据类型 时间序列数据(time series data) 横截面数据(cross-s

time series prediction：时间序列预报

相依时间序列:Dependence Time Series | 时间序列预报:time series prediction | 混沌时间序列:chaotic time series