In the final chapter, we mine stock trading data using time series method, find out the model and outliers in the data and, at last, we show the more exact forecasting model and outlier mining method.
第五章利用时间序列的方法对证券交易数据进行了挖掘，找出了数据中的模式和异常，相对传统方法而言，给出了更精确的预测模型和异常挖掘方法。
Finally, based on researches above, we implement the data mining system of the stock with aggregation database and object oriented technology.
最后，在以上研究的基础上，集合数据库、面向对象等技术实现了股票数据挖掘系统。
Finally, we discuss the application of data mining for the analysis and decision of stock.
然后通过实例讨论了数据采掘在股票分析与决策系统中的应用。
This research adopts techniques of data mining and artificial intelligence to build up analysis models from the historical data of stock market to assist investment decisions.
摘要：本研究利用资料探勘及人工智慧技术，借由股票市场已过的资料来建立分析模型，以协助投资决策。
Then do a thorough and meticulous research into the theory of data mining. At last discuss the following three aspects:First of all, focus on the research into financial indexes of stock.
本文首先介绍了股票分析与预测的背景知识和方法，其次对数据挖掘理论做了深入细致的研究，然后着重从以下三个方面展开讨论。
Stock price behavior data mining has aroused great concern in research of computer science, economy, machine learning and other fields.
股票价格行为数据挖掘激发了计算机科学、机器学习及其他领域研究的广泛关注。
In this paper, we propose to combine stock price and network consensus and create a stock behavior data mining platform based on network consensus.
本文提出了股票价格充分融合网络共识的策略，构建了基于网络共识的股票价格行为数据挖掘平台。
A new data-mining algorithm based on dynamic programming and dynamic time warping function was proposed and applied in technical analysis of stock market.
提出了一种基于动态规划和动态时间弯折函数的数据挖掘算法，并应用该算法对股市进行技术分析。
A new data-mining algorithm based on dynamic programming and dynamic time warping function was proposed and applied in technical analysis of stock market.
提出了一种基于动态规划和动态时间弯折函数的数据挖掘算法，并应用该算法对股市进行技术分析。
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