City gas load is a multi-mode and complicated engineering system. This paper analyzes and estimates the gas load of Shenzhen by using method of time series analysis.
城市燃气负荷是一个多工况、复杂的工程系统，本文采用时间序列方法对深圳的燃气负荷进行了分析和预测。
Two kinds of models are derived; load prediction model based on building model recognition and load prediction model based on time series analysis.
提出了两种类型负荷预报模型，基于建筑模型辩识的负荷预报法和基于时间序列的负荷预报法。
In this paper, the theory and method of fuzzy time series analysis are presented, the model form and the parameters estimate problem are studied.
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。
Next, the thesis analysis the characterization of Roundtrip time delay (RTT). The RTT time series collected from the Internet are studied statistically by using both linear and nonlinear methods.
其次，对网络时延(RTT)特性进行了分析，利用线性和非线性方法对从互联网上采集的RTT时间序列进行统计分析。
This paper puts forward a seasonal neural network model to curve fitting analysis for nonlinearity and predict for the seasonal time series of outpatient amount.
本文提出一种利用季节性神经网络模型对医院门诊量进行非线性曲线拟合分析和预测。
At present, outlier mining has attached a great importance in the field of time series analysis.
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。
Application of digital filtering and time series analysis to earthquake precursory processing is summarized in this paper.
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。
Finally, the results show the methods can effectively come into being regression analysis model of time-series data streams, and fulfill the prediction of future data streams.
最后，试验分析展示了研究结果能够有效地产生时间序列数据流的回归模型和实现数据流未来数据的预测。
Linear growth model is widely used in the analysis and forecast of time series in economic and biological fields.
线性增长型模型被广泛应用于经济领域和对生物信号的时间序列的分析和预报。
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。
The Dynamic time series period analysis and prediction model analyses a serial-typed time series from the point of statistics, finding out the law. thereby succeeding in predicting the future.
动态时间序列周期分析预测模型是从数理统计的角度对值为连续型的时间序列进行分析，发现规律，从而成功预测未来。
The obtained conclusion provides a premise for the application of chaotic time series analysis in well-log facies recognition.
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。
This article discusses the main problem of time series analysis in the field of forecast application and programming.
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。
Using the methods of time series spectral analysis and Kalman filter, this article discussed the additive problems of two stochastic processes, mainly Auto Regression Moving Average (ARMA) processes.
本文利用时间序列谱分析和卡尔曼滤波的方法讨论了两个随机过程，主要是自回归滑动平均(ARMA)过程，的叠加问题。
Through the simulating experiments and the analysis of Angle motion time series, the detailed analyzed conclusions about flight instability mechanism are presented.
经过仿真实验，分析角运动的时间序列，给出了比较详尽的飞行不稳定机制分析结果。
Then, we make prediction with moving exponential average model after the analysis of the travel time series. Finally, we present reasonable justification.
通过分析行程时间时间序列的时变特性，利用指数平滑模型进行预测，最后提出合理的修正方法。
So the influence of high-frequency noise is analyzed by the analysis of power spectra, coherent structure, probability distribution of time series and differences of them.
针对这些现象，从谱、相干结构、时间序列本身的概率分布和标量差的概率分布，分析了高频噪声的影响。
Methods Using the time series analysis of cucumber downy mildew disease, to explore the forecasting method from the Angle of methodology.
方法：采用黄瓜霜霉病病情指数时间序列从方法学的角度进行预测方法研究。
How to identify chaos is the foundation of analysis, prediction and control of nonlinear time series.
识别混沌是对非线性时间序列进行分析、预测、控制的基础。
In the analysis of time series, the surrogate data test is often performed in order to investigate nonlinearity in the data.
替代数据检验法是检验时间序列中是否存在确定性非线性成分的重要统计方法。
The time series analysis with multiple equations is an important part of time series analysis, which is widely applied in the field of macro-economics and draws more and more attention in the world.
多方程时间序列分析是时间序列分析的重要组成部分，它在宏观经济研究领域有着广泛的应用，越来越受到世界各国的关注。
The reliability of the time-series analysis method in processing unsteady random signals is verited.
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；
This paper focuses on the application of nonlinear dynamical methods in the analysis of time series.
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。
Based on the state space analysis, the time series analysis method for identification of the stochastic continuous signals, proved as consistent convergence, is given.
基于状态空间分析，给出了连续随机信号建模的时间序列分析方法，并证明了参数估计的一致收敛性。
There are many branches in the field of time series analysis using nonlinear tools, and nonlinear dynamical methods is one of them that springs up in these years.
可以用来研究时间序列的非线性工具有许多种，而其中非线性动力学方法则是近年来兴起的一个重要分支。
Selecting the proper classification features, the primary crop types in North China could be identified through the analysis of the vegetation index time series in growth season.
通过对作物生长期内植被指数变化曲线分析，选择合适的分类特征，进行华北地区主要作物类型识别。
The structure mutation during the economic process has very important influence on the analysis of non-smoothed time series.
经济过程中的结构突变，对非平稳时间序列的分析具有非常重要的影响。
The time-space series analysis method containing randomicity on the base of continuity is formed after the analysis systems of time series, space series and fractals are compared.
对比研究了时间序列、空间序列、分形的分析体系，提出了在连续性基础上包容随机性的时空序列分析方法。
Furthermore, a random drift error model for IFOG is built by the method of time series analysis.
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.
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。
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