【作者】 曲吉林；

【Author】 QU Jilin;School of Accounting, Shandong University of Finance;

【机构】 山东财政学院会计学院；

【摘要】 金融数据异常检测对于全面、准确地认识和把握金融市场的波动规律,加强宏观管理和调控,具有十分重要的理论价值和实践意义。传统的金融数据异常检测方法主要是基于统计学的模型法,主要适用于低频金融数据的异常波动分析。本文提出一种基于数据挖掘技术的金融数据异常检测方法,可以适用于高频和低频金融数据,具有检测准确性和效率高等优势。更多还原

【Abstract】 Outliers detection has wide application for financial surveillance. The Traditional outlier detection method is based on statistical models, such as ARMA and ARCH, which require special hypotheses. The statistical models are inappropriate to apply to complex financial data, such as high frequency data. This paper introduces a new data mining method to detect outliers for financial surveillance. Based on the Voronoi diagram, we propose a novel outlier detection method, which called Voronoi based Outlier Detection(VOD), to provide efficient and effective outlier detection in financial data.更多还原