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Feature extraction for chart pattern classification in financial time series
Knowledge and Information Systems ( IF 2.7 ) Pub Date : 2021-05-07 , DOI: 10.1007/s10115-021-01569-1
Yuechu Zheng , Yain-Whar Si , Raymond Wong

Extracting shape-related features from a given query subsequence is a crucial preprocessing step for chart pattern matching in rule-based, template-based and hybrid pattern classification methods. The extracted features can significantly influence the accuracy of pattern recognition tasks during the data mining process. Although shape-related features are widely used for chart pattern matching in financial time series, the intrinsic properties of these features and their relationships to the patterns are rarely investigated in research community. This paper aims to formally identify shape-related features used in chart patterns and investigates their impact on chart pattern classifications in financial time series. In this paper, we describe a comprehensive analysis of 14 shape-related features which can be used to classify 41 known chart patterns in technical analysis domain. In order to evaluate their effectiveness, shape-related features are then translated into rules for chart pattern classification. We perform extensive experiments on real datasets containing historical price data of 24 stocks/indices to analyze the effectiveness of the rules. Experimental results reveal that the features put forward in this paper can be effectively used for recognizing chart patterns in financial time series. Our analysis also reveals that high-level features can be hierarchically composed from low-level features. Hierarchical composition allows construction of complex chart patterns from features identified in this paper. We hope that the features identified in this paper can be used as a reference model for the future research in chart pattern analysis.



中文翻译:

金融时间序列中图表模式分类的特征提取

从给定查询子序列中提取形状相关的特征是基于规则,基于模板和混合模式分类方法中图表模式匹配的关键预处理步骤。提取的特征会在数据挖掘过程中极大地影响模式识别任务的准确性。尽管与形状相关的特征已广泛用于金融时间序列中的图表模式匹配,但是在研究界很少研究这些特征的内在属性及其与模式的关系。本文旨在正式确定图表模式中使用的形状相关特征,并研究它们对金融时间序列中图表模式分类的影响。在本文中,我们描述了对14种形状相关特征的综合分析,这些特征可用于对技术分析领域中的41种已知图表模式进行分类。为了评估其有效性,然后将与形状相关的特征转换为用于图表模式分类的规则。我们对包含24种股票/指数的历史价格数据的真实数据集进行了广泛的实验,以分析规则的有效性。实验结果表明,本文提出的特征可以有效地用于识别金融时间序列中的图表模式。我们的分析还表明,高级功能可以由低级功能分层组成。通过分层组合,可以根据本文确定的功能构建复杂的图表模式。

更新日期:2021-05-07
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