The output time series of the possibilistic system, in which the datum are given with the form of fuzzy Numbers, is called fuzzy time series.
用模糊数形式表示的可能性系统的输出时间序列称作模糊时间序列。
Moving average method is one of time series forecasting method, if time series have no apparent tendency moving, using moving average method can accurately reflect actual situation.
移动平均法是一种时间序列预测法，当时间序列没有明显的趋势变动时，使用移动平均就能够准确地反映实际情况。
The basic idea and some kinds of the common time series models and the development characteristics of time series are explained in detail.
详细阐明了时间序列的基本思想、几种基本时序模型和时序动态特征，讨论分析了如何进行模型识别、模型参数计算和模型的定阶。
The process of knowledge discovery in time series includes preprocessing of time series data, attributes reduction and rules extraction.
知识发现的过程包括时间序列数据预处理、属性约简和规则抽取三部分。
The similarity pattern query about time series is one of the research hotspots in knowledge discovering in the time series database.
时间序列相似性模式搜索是营销时间序列数据仓库中知识发现领域的一个研究热点。
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。
Combined with certain type time series recount multiplicity model and random type ARMA model, establish the time series model of the death rate in Chongqing urban area.
应用确定型的时间序列分解法乘法模型与随机型的arma模型相结合，建立重庆市主城区人口死亡率的时间序列模型。
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 problem can be described as: searching the sequence most similar to a given time series from a large time series database.
该问题可描述为给定某个的时间序列，要求从一个大型时间序列数据库中找出与之最相似的序列。
The fuzzy time series forecasting differ from classic time series forecasting is lead in the conception, named membership function which contribute much to figure the method.
模糊时间序列法不同于经典时间预测之处在于其引入了隶属函数的概念，在序列的预测演算中起到重要作用。
Wavelet network based nonlinear time series prediction model is submitted, and nonlinear time series prediction and its application in fault prediction are discussed in this paper.
本文提出了基于小波网络的非线性时间序列预报模型，探讨了非线性时间序列预报在故障预报中的应用。
Test of nonlinearity of time series is very important for nonlinear time series analysis and study of chaotic dynamics.
时间序列的非线性检测对于非线性时间序列分析、混沌特性研究有着重要意义。
We give multifractal detrended fluctuation analysis and Hlder analysis of discrete time series and use them to study the temperature time series fluctuations.
给出了离散时间序列多重分形除趋势涨落分析方法和霍尔德指数的计算方法，并用它们研究了气温时间序列。
Firstly, making the time series continuous through inserting data, and secondly removing the secular displacement rate from the time series data through linear fitness.
首先对时间序列中不连续的数据进行内插处理，并通过线性拟合从时间序列中去掉长期滑动速率的影响。
Among these research fields, time series data mining is a rather complex branch, which is a technique that extracts the most valuable information from large amount of history time series data.
而在这其中时间序列数据挖掘是面向特殊应用数据挖掘领域中比较复杂的一个分支，主要研究从大量时间序列历史数据中挖掘有价值信息的方法和相关技术。
The time series analysis can also be used in ship pitching and heaving time series prediction. These indicate that the prediction method is valuable for engineering practice.
时间序列分析法亦可用于船舶纵摇、艏摇的时间序列预报，该方法在工程中具有很大的实用价值。
Mapping the raw time series data to a modality space effectively is a critical problem in time series similarity search.
将时序数据有效地映射到特征空间是时间序列相似性搜索的一个关键问题。
Different from the existent noise reduction methods in nonlinear time series, the method based on principal manifold learning emphasized more on the global structure of time series.
与现有的非线性时间序列消噪算法不同，基于主流形的消噪算法更强调时间序列的整体结构。
A weighted method of customer's time series is proposed and statistical features of time series are adopted for customer clustering, which make each group of customers have similar sequence feature.
提出了客户时间序列的加权处理方法，并应用客户时间序列的统计特征作为聚类特征向量，采用混合式遗传算法对客户聚类，使每一类客户具有相似的时序特征。
Time series analysis based on neural networks theory cross through traditional frame of subjective model draw out prediction on the inner rules of linear time series data.
基于前向型神经网络理论的时间序列分析跳出了传统的建立主观模型的局限，通过时间序列的内在规律作出分析与预测。
Recently the study on data mining of time series mainly concentrates on both the similarity search in a time series database and the pattern mining from a time series.
时间序列存在于社会的各个领域，对于时间序列数据挖掘的研究目前主要集中在相似性搜索和模式挖掘上。
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。
In this paper the predictability of drillability time series was analyzed using fractal method based on the study of drillability time series characters.
本文从研究可钻性时间序列特征出发，应用分形几何方法，分析了可钻性时序的可预测性。
Time series modeling and identification techniques were analyzed and the ARMA time series model based on robust LS-SVM algorithm was proposed.
对时序数据建模与辨识技术进行了分析，提出了使用鲁棒LS-SVM算法建立ARMA时序预测模型。
In the paper, we construct a new seasonal adjustment method of time series on the basis of the structural time series model.
建立一种基于结构时间序列模型的新的时间序列季节调整方法。
Using linear regressive models (e. g. AR, ARMA model) to fit and predict the climatic time series, the results are not sufficiently good because there exist nonlinear variations in the time series.
在用AR、ARMA等线性模式对气候序列进行拟合和预报时，由于气候序列中存在着非线性变化，所以拟合和预报效果往往不太理想。
The existing algorithms to extract trend features based on time series piecewise linearization representation cannot extract completely correct basic trend features of time series.
根据新的目标函数，设计了一种重要点和自底向上分割相结合的时间序列分段线性化趋势特征提取方法。
For the problems of continuity, uncertainty and fuzziness in the time-series of the network management alarm database, this pa-per puts forward a new mining method based on time-series rules.
该文针对网管告警数据库中时间序列存在的连续性、不确定性和模糊性问题，提出了一种基于时态关联规则挖掘告警库的新方法。
Financial time series has high randomicity and nonlinearity. Neural network is quite suitable in the process of financial time series data for its good ability of nonlinear mapping and generalization.
金融时间序列具有很强的随机性和非线性性，而神经网络具有良好的非线性映射能力及自适应、自学习和良好的泛化能力，因此非常适合处理金融时间序列这样的数据。
Financial time series has high randomicity and nonlinearity. Neural network is quite suitable in the process of financial time series data for its good ability of nonlinear mapping and generalization.
金融时间序列具有很强的随机性和非线性性，而神经网络具有良好的非线性映射能力及自适应、自学习和良好的泛化能力，因此非常适合处理金融时间序列这样的数据。
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