基于最邻近算法的财政数据异常值实时监测方法研究

打开文本图片集

中图分类号：TP393 文献标志码：A

文章编码：1672-7274（2025）04-0013-04

Abstract： The existing real-time monitoring methods for abnormal values in financial data have low accuracy and high false alarmrates，which leads to inaccurate monitoring of abnormal values in financial data and has certain limitations.This article proposes a real-time monitoring method for financial data outliers basedon nearest neighbor algorithm.Firstly，bycalculatingthelocaldensityandminimumdistance，selectingtheRBFkernelfunction，areal-time monitoring modelfor financial dataoutliers is established basedontheclustering results.Secondly，the intensityratio ofthesetreference windowand investigation window iscalculated to extract abnormal paterns in fiscal data.Finally， based on the elbow rule curve and follwing acertain process，the monitoring task of abnormal values in financial data is completed.The experimentalresultsshowedthatusing thereal-time monitoring methodforfinancialdataoutliers basedon the nearest neighbor algorithm，the monitoring accuracy was over 95% ，and its average falsealarm rate was 3.21%

Keywords： local density; nearest neighbor algorithm; RBF kernel function; intensity ratio; elbow rule

在财政数据管理中如何准确、高效地识别和处理异常值成为保障数据质量[1]、预防财务风险中亟待解决的问题。（剩余3110字）