{"title":"Unsupervised Outlier Detection in Time Series Data","authors":"Z. Ferdousi, Akira Maeda","doi":"10.1109/ICDEW.2006.157","DOIUrl":null,"url":null,"abstract":"Fraud detection is of great importance to financial institutions. This paper is concerned with the problem of finding outliers in time series financial data using Peer Group Analysis (PGA), which is an unsupervised technique for fraud detection. The objective of PGA is to characterize the expected pattern of behavior around the target sequence in terms of the behavior of similar objects, and then to detect any difference in evolution between the expected pattern and the target. The tool has been applied to the stock market data, which has been collected from Bangladesh Stock Exchange to assess its performance in stock fraud detection. We observed PGA can detect those brokers who suddenly start selling the stock in a different way to other brokers to whom they were previously similar. We also applied t-statistics to find the deviations effectively.","PeriodicalId":331953,"journal":{"name":"22nd International Conference on Data Engineering Workshops (ICDEW'06)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"90","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"22nd International Conference on Data Engineering Workshops (ICDEW'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDEW.2006.157","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 90
Abstract
Fraud detection is of great importance to financial institutions. This paper is concerned with the problem of finding outliers in time series financial data using Peer Group Analysis (PGA), which is an unsupervised technique for fraud detection. The objective of PGA is to characterize the expected pattern of behavior around the target sequence in terms of the behavior of similar objects, and then to detect any difference in evolution between the expected pattern and the target. The tool has been applied to the stock market data, which has been collected from Bangladesh Stock Exchange to assess its performance in stock fraud detection. We observed PGA can detect those brokers who suddenly start selling the stock in a different way to other brokers to whom they were previously similar. We also applied t-statistics to find the deviations effectively.
欺诈检测对金融机构来说非常重要。本文研究了一种无监督的欺诈检测技术——对等群分析(Peer Group Analysis, PGA)在时间序列金融数据中发现异常值的问题。PGA的目标是根据相似对象的行为来描述目标序列周围的预期行为模式,然后检测预期模式与目标之间的进化差异。该工具已应用于股票市场数据,这些数据已从孟加拉国证券交易所收集,以评估其在股票欺诈检测方面的表现。我们观察到PGA可以检测到那些突然开始以不同的方式出售股票的经纪人,而这些经纪人之前与他们相似。我们还应用了t统计量来有效地找到偏差。