R还具有更多用于时间序列分析的工具。
R provides still more tools for time series analysis. For example, we can plot the autocorrelation function for the living room temperature.
载体，进行测序和序列分析。
动态编程是在序列分析中经常使用的一种算法技术。
Dynamic programming is an algorithmic technique used commonly in sequence analysis.
互补；克隆分子；序列分析；基因表达。
DNA, complementary; cloning, molecular; sequence analysis; gene expression.
目的克隆人乳铁蛋白基因并进行基因序列分析。
Objective to clone the gene of human lactoferrin and analyze its sequence.
对其序列分析表明它具有启动子初级结构的基本特征。
Sequence analysis showed that it contained the basic characteristic of primary structure of promoter.
结果序列分析表明，所扩增的片段均属于HIV基因。
Results Sequences analysis showed that all the fragments amplified were HIV gene.
最后给出了用于时间序列分析的动态贝叶斯网络的实例。
In the last, we give an example of dynamical Bayesian networks for time series data analysis.
本文主要研究非线性动力学方法在时间序列分析中的应用。
This paper focuses on the application of nonlinear dynamical methods in the analysis of time series.
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?
验证了时间序列分析方法在非平稳随机信号处理方面的可靠性；
The reliability of the time-series analysis method in processing unsteady random signals is verited.
目前，在时间序列分析领域，孤立点的挖掘越来越多的受到重视。
At present, outlier mining has attached a great importance in the field of time series analysis.
快速影像匹配是进行影像时间序列分析与飞行器导航的重要方法。
Fast image matching is an important method for aircraft navigation and motion analysis.
采用非线性混沌时间序列预报，不同于传统的时间序列分析方法。
It differs from traditional time-sequence analysis methods in which nonlinear chaotic time-sequence prediction is used.
实例结果分析表明，随机集模型是一种很好的时间序列分析方法。
The example result shows that the random-sets method is a nice approach to time series analysis.
本文概述了数字滤波和时间序列分析在地震前兆信息处理中的应用。
Application of digital filtering and time series analysis to earthquake precursory processing is summarized in this paper.
本文讨论的主要问题是时间序列分析在预测领域的应用及编程实现。
This article discusses the main problem of time series analysis in the field of forecast application and programming.
本研究还选取了部分形态学上的近似种和疑难种进行ITS序列分析。
Finally, in this study, some similar species in morphology were chosen for ITS sequences analysis.
预报误差校正是基于时间序列分析、参数估计和最优预报原理形成的。
The prediction error correction is based on time series analysis, parameters estimation and optimum prediction principle.
结合现代时间序列分析方法，并根据新息模型设计了状态最优滤波器。
An ARMA innovation model and the state optimal filter are designed by modern time series analysis method.
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.
这一结论为混沌时间序列分析方法应用于测井曲线识别领域提供了前提条件。
The obtained conclusion provides a premise for the application of chaotic time series analysis in well-log facies recognition.
时间序列分析可以通过差分、周期的方法，对植物种群的增长进行模拟与预测。
Time series model can simulate and predict the increase of Quercus variabilis population by the difference, periodic method.
本文提出了模糊时间序列分析的理论和方法，研究了模型形式及其参数估计问题。
In this paper, the theory and method of fuzzy time series analysis are presented, the model form and the parameters estimate problem are studied.
生物序列分析的主要研究内容包括序列比对、蛋白质结构预测、基因组序列分析等。
The biological sequence analysis research content mainly includes the sequence alignment, the protein structure prediction, and the genome sequence analysis etc.
时间序列分析是动态数据分析的重要方法，在多学科领域中得到广泛的研究和运用。
Time series analysis is an important method of the dynamic data analysis which has extensive researches and usages in many subject fields.
结合这两个种的染色体倍性鉴定、序列分析和栽培观察实验，初步确定划分为两个种。
Acoording the ploidy analysis of chromosome, sequences and the survey of cultivation in field, Opisthopappus is primarily divided into two species.
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.
时间序列分析是统计学的分支之一，它的研究对象是离散有序数列的变化特征和变化趋势。
As one of the branches of statistics, time series analysis focuses on the variation characters and trend of discrete ordered data series mainly.
时间序列分析是统计学的分支之一，它的研究对象是离散有序数列的变化特征和变化趋势。
As one of the branches of statistics, time series analysis focuses on the variation characters and trend of discrete ordered data series mainly.
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