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股票时间
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  stock time
     Association rules mining in stock time series model
     股票时间序列模型的关联规则挖掘
短句来源
     Research on Mining Association Rules from Stock Time Series Data
     基于股票时间序列数据的关联规则挖掘研究
短句来源
     3. Discuss the data mining model based on Rough Set and data reduct algorithms. A new algorithm, which is based on Rough Set, aiming to discover association rules in stock time series data is presented.
     第三,探讨了基于Rough集的数据挖掘模型与数据约简算法,在此基础上,提出了一种基于Rough集的股票时间序列关联规则挖掘算法。
短句来源
     Mining Trading Rules from Stock Time Series Based on Charting Patterns
     基于序列模板的股票时间序列交易决策规则挖掘
短句来源
     This paper proposes a method by which investors can acquire some trading rules from stock time series based on their experience and intuition.
     研究一种可由投资者依据其经验与直觉,从股票时间序列中挖掘交易决策规则的方法。
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  “股票时间”译为未确定词的双语例句
     Knowledge Discovery in Stock Market Time Sieres Based on Rough Set
     粗集理论对股票时间序列的知识发现
短句来源
     Knowledge Discovery in Stock Market Time Series Based on Regularized Feedforward Neural Networks
     基于正则前馈神经网络的股票时间序列数据库的知识发现
短句来源
     Stock Market Time Series Data Mining Based on Regularized Neural Network and Rough Set
     正则化训练的神经网络与粗集理论相结合的股票时间序列数据挖掘技术
短句来源
     This paper shows the linear model of time sequence and the ARCH model and finds that the GARCH model could describe the fluctuation of price in some degrees.
     文章给出了股票时间序列的线性模型和股票价格的自回归条件异方差模型(ARCH),发现GARCH族模型在一定程度上能够描述股价的波动情况。
短句来源
     After applying the association rules mining to the discovery of timeseries of stocks, the author add to the constraints of time slices, time intervals and trend rules for the time series of stocks.
     2.把关联规则挖掘技术应用到股票时间序列的发现上,并对股票时间序列增加时间段约束,时间间隔约束和走势模式约束。
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  相似匹配句对
     Time is...
     时间
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     Time
     时间
短句来源
     Stock Index Analysis Based on Time Series Models
     股票指数的时间序列模型分析
短句来源
     Dynamic analysis about time series of stock market price
     股票市场价格时间序列的动力学分析
短句来源
     A Brief Study about Zhaoxin Stock
     昭信股票浅析
短句来源
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  stock time
The results of simulations are presented regarding harvested resource stock time path.
      
It also shows that policies which induce a stabilizing effect in stock time series will generally provide greater welfare gains.
      
Encouraging simulation results on the Hong Kong Hang Seng Index series and a few stock time series are reported.
      
In this manner, we obtain properly aligned stock time series of identical length.
      
Mainly as a consequence of the close to random-walk behaviour of a stock time series.
      
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A method of stock price prediction is presented by hypothesis of stock market being non-linear dynamic system and analyzing method of chaos theory for chaos time series in this paper. Meanwhile, structures of radial basic function (RBF) network and pairs of training samples are determined by embedding dimension and delay time of reconstruct phase space respectively. Predicting results for real world stock time series show that the method is able to do effectively short-term prediction. In comparison with traditional...

A method of stock price prediction is presented by hypothesis of stock market being non-linear dynamic system and analyzing method of chaos theory for chaos time series in this paper. Meanwhile, structures of radial basic function (RBF) network and pairs of training samples are determined by embedding dimension and delay time of reconstruct phase space respectively. Predicting results for real world stock time series show that the method is able to do effectively short-term prediction. In comparison with traditional forward feedback neural network (BP), the method can make better predicting performance, thus it can be widely used in stock price prediction.

根据股票市场是非线性动力系统的假设,利用混沌理论对混沌时间序列的分析方法,提出了股票价格预测方法。同时利用重构相空间的嵌入维数和延迟时间分别确定经向基函数模型网络的结构和训练样本对,对实际的股票时间序列预测结果表明,该方法能有效地进行短期预测,并与前馈神经网络模型相比,可得到较好的预测结果,因而在股票时间序列预测中有广泛的实用价值。

There are many association rules among multiple financial time series.In this paper,a new algorithm named ES-Apriori will be presented to mine inter -time series association rules.This algorithm needs to search the whole database only time ,with the rational arrangement of memory usage it can successfully mine the association rules among time series.Experiments have shown that this method can efficiently analyse the time series of Chinese Stock Market.

多元金融时间序列之间是互相影响的。该文就跨时间序列的关联规则挖掘提出一种新方法:ES-Apriori,此方法通过减少数据库扫描次数,优化内存分配,能够高效地分析多元时间序列之间的关联规则。试验表明,用此方法分析中国证券市场的股票时间序列非常有效。

This paper uses the learning algorithm of feedforward neural networks based on the regularized least squares on the knowledge discovery on time series databases. The algorithm improves the generalization performance of feedforward neural networks through combining the regularization and pruning technology. It demonstrates the method on the temporal rule discovery of stock market time series database. The process of knowledge discovery includes preprocessing of time series data and data mining (rule discovery)....

This paper uses the learning algorithm of feedforward neural networks based on the regularized least squares on the knowledge discovery on time series databases. The algorithm improves the generalization performance of feedforward neural networks through combining the regularization and pruning technology. It demonstrates the method on the temporal rule discovery of stock market time series database. The process of knowledge discovery includes preprocessing of time series data and data mining (rule discovery). The experiment demonstrates the effectiveness of the algorithm.

将正则最小二乘前馈网络学习算法应用于时间序列的知识发现。正则最小二乘算法将正则化网络和节点删除算法结合起来,大大提高了前馈网络的泛化性能。将其应用于股票时间序列数据库的暂态规则的知识发现,发现过程包括时间序列数据库预处理和数据挖掘(规则发现)两部分,实验结果表明预测效果良好。

 
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