波动持续性是广泛存在于经济和金融时间序列的一类普遍现象。
Volatility persistence, which have been found in many of time series of economic and finance, indicates that the risk is dependent each other.
非线性理论在刻画金融时间序列的波动方面有着非常重要的作用。
The non-linear theory has been playing an important role in describing volatility of financial time series.
金融时间序列的分析与建模是金融计量学的一个很重要的研究领域。
The analysis and modeling of the financial time series is a very important study realm in financial metrology.
基于上述原因，本文将数据挖掘和金融时间序列结合在一起进行研究。
Based on above analysis, this paper integrates the study of data mining and financial time series.
金融时间序列模型的变点分析是一类重要的统计问题，它引起众多学者的关注。
The change-point analysis in financial time series has been regarded as one of the core areas of research in statistics.
数值实验表明，SVR方法对非平稳的金融时间序列具有良好的建模和泛化能力。
Numerical test results show that SVR has good ability of modeling nonstationary financial time series and good generalization under small data set available.
本文主要利用金融时间序列arch模型研究国内外期铜市场的波动性及持续性。
This paper is aiming at study the volatility and durative of the local and oversea copper futures market by the time series ARCH model.
方法用于对非平稳金融时间序列进行了符号化转换，实验结果表明该方法是有效的。
The proposed method is applied to unsteady financial time series symbolization. Experimental result shows that the method is effective.
波动性不仅普遍存在于金融时间序列之中，而且也是金融市场研究中的一个核心问题。
The volatility is not only a universal phenomenon existing in the financial time series, but also a core research question to describing the financial market.
因此，如何有效地刻画金融时间序列波动的动态行为一直是金融计量学研究的热点问题。
Therefore, how to describe the dynamic behavior of the financial time series' fluctuation well is always a hot research point in Financial Econometrics.
提出了一种新的概率函数计算方法，用于研究金融时间序列在方差波动方面的多重分形特征。
A new probabilistic function for studying the multi-fractal features on the volatility of variance of financial time series is proposed.
为将基于窗谱估计的模型验证技术应用于金融时间序列领域，以解决金融时间序列模型的设定正确性。
Thus, the window spectrum estimation technique provides more effective model validation than the traditional back-test m.
而金融数据中的非线性问题和金融时间序列分析中的非线性经济计量模型又是这个领域中全新的研究课题。
But nonlinear problem in financial data and nonlinear economic metric model in financial time series is an all new research topic in this realm.
相似性度量是金融时间序列挖掘中的一项关键技术，但现有的度量方法不适合分析小规模的金融多元时间序列。
Similarity measure is a key technology of time series mining, whose existing methods are not available for the analysis of small-scale multivariate time series.
MATLAB是优秀的数学计算工具，本文阐述并举例说明如何利用MATLAB来对金融时间序列进行分析及建模。
MATLAB is an outstanding mathematical computing tool. In this paper, we expatiate how to analyze and model financial time sequences with MATLAB.
近年来GARCH模型被广泛地用于对变动频率很高的金融时间序列建模，它能较好地抓住此类时间序列的动态特征。
At present, GARCH type models have been employed to model these high frequency financial time series due to their ability to capture the dynamic characteristics.
传统的金融时间序列认为趋势项是确定性的函数，本文对此提出一种新的模型，并且设计了分段最小二乘方法和两个算法提取出趋势路径。
For this problem, this paper develop a new model and design the piece wise least square method and two algorithms to strip off the trend.
笔者首先讨论了在金融时间序列的考察中从一元GARCH模型扩展到多元GARCH模型的必要性。分析了多元GARCH模型在金融建模中的重要作用。
First of all, the author discusses the extension from univariate GARCH to multivariate GARCH model and the important role of the MGARCH model in the modern financial research.
提出了用小波神经网络（WNN）来定量研究高频金融时间序列“日历效应”，通过比较发现WNN是比弹性傅立叶形式（FFF）回归技术更具优势的方法。
The paper proposes application of Wavelet Neural Network in high-frequency time series calendar effects' study. At last, the paper proves that WNN is better than classical FFF regression.
在这篇文章里，笔者主要关注当同时考察多支金融时间序列的波动时，多元GARCH模型相比于一元GARCH模型而言，对相关系数和波动性的更好的描述。
In this article, the author concerned with a better description of the volatility and correlations under multivariate GARCH model compared with univariate GARCH model.
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.
时间序列的波动持续性建模理论和方法是经济金融领域风险分析的一种强有力的工具。
The theory and method of modeling volatility persistence of time series is a powerful tool in analyzing the risk of economic and finance market.
主要研究和教学领域：时间序列计量经济学、实证金融。
Research and teaching: time series econometrics and empirical finance.
金融高频数据是一种不等间隔的时间序列，现有的相似性查找技术对高频数据的处理效果不佳。
The existing methods of similarity search are not suitable for high frequency financial data, which is a kind of non-interval time series.
由于该模型被认为是最集中反映了金融市场数据方差变化的特点而被广泛应用于金融数据时间序列分析中。
Because these models can reflect the feature of the financial market well, they have been widely applied in the time series analysis on financial data.
许多经济学家们不懈努力，孜孜以求，试图找到一个能够全面地刻划金融数据这些特性的时间序列模型。
Many economists keep on working hard, making a great effort to try to find a time series model which can capture most of these characteristics of financial data.
在金融系统研究中，经常分析多维时间序列之间的相关关系，如短期信息、长期均衡关系。
In the financial system research, analyze the correlation relations between the multi-dimensional time series frequently, like short-term information, long-term balanced relations.
在金融系统研究中，经常分析多维时间序列之间的相关关系，如短期信息、长期均衡关系。
In the financial system research, analyze the correlation relations between the multi-dimensional time series frequently, like short-term information, long-term balanced relations.
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