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。
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.
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。
Data mining are used to analyze the foreign exchange rate time series and acquire the correct, implicated and hidden information, which has practical significance in the financial field.
利用数据挖掘技术分析外汇汇率时间序列，从时间序列中获得正确的、隐含的、潜在的信息对于金融领域研究具有重要的现实意义。
The existing methods of similarity search are not suitable for high frequency financial data, which is a kind of non-interval time series.
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。
But nonlinear problem in financial data and nonlinear economic metric model in financial time series is an all new research topic in this realm.
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。
Numerical test results show that SVR has good ability of modeling nonstationary financial time series and good generalization under small data set available.
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。
Based on above analysis, this paper integrates the study of data mining and financial time series.
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。
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.
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。
High frequency time series is referred to financial data which is sampled with interval of one hour, one minute even one second.
高频时间序列通常是指以每小时、每分钟甚至每秒为频率所采集的金融类数据；
High frequency time series is referred to financial data which is sampled with interval of one hour, one minute even one second.
高频时间序列通常是指以每小时、每分钟甚至每秒为频率所采集的金融类数据；
应用推荐