一种基于混沌预测的离群时间序列检测方法  

A method for outlier time series detection based on one-step chaotic prediction

作　　者：王斌[1] 汤俊[2] 陶也青 

机构地区：[1]江西赣江职业技术学院,江西南昌330108 [2]中南财经政法大学信息学院,湖北武汉430074

出　　处：《武汉大学学报（工学版）》2010年第2期265-268,共4页Engineering Journal of Wuhan University

基　　金：国家社科基金项目(编号:09BTJ002)

摘　　要：提出一种基于混沌行为预测的金融交易离群检测方法.通过对短期金融交易时间序列的混沌分析,建立其未来行为趋势预期机制.利用RBF神经网络构建金融交易序列的拟合函数,以此进行一步行为预测,比较实际结果与预测结果的偏差,从而得到离群判别.合成与真实数据的实验表明了该方法的有效性.Traditional outlier detection has been dominated by statistical method to find out those unusual behaviors deviating from statistical profile.We propose a new outlier detection method based on chaotic behavior prediction theory.The method firstly tries to understand the nature of short-term financial transaction operations through time series chaotic analysis,and then provides a mechanism of forecasting the next step result.We construct an approximator for financial operations and a predictor for one-step behavior by employing radial basis function neural network,to detect those behaviors apparently contradicted to the prediction.Experiments on synthetic data mixed with real-world data and simulated outlier data demonstrate encouraging performance in outlier extraction.

关 键 词：可疑金融交易 混沌预测 RBF神经网络 离群检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]