Multi-resolution Time Series Discord Discovery

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10306)

Abstract

Discord Discovery is a recent approach for anomaly detection in time series that has attracted much research because of the wide variety of real-world applications in monitoring systems. However, finding anomalies by different levels of resolution has received little attention in this research line. In this paper, we introduce a multi-resolution representation based on local trends and mean values of the time series. We require the level of resolution as parameter, but it can be automatically computed if we consider the maximum resolution of the time series. In order to provide a useful representation for discord discovery, we propose dissimilarity measures for achieving high effective results, and a symbolic representation based on SAX technique for efficient searches using a multi-resolution indexing scheme. We evaluate our method over a diversity of data domains achieving a better performance compared with some of the best-known classic techniques.

Keywords

Time series Anomaly detection Discord discovery Indexing 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of ChileSantiagoChile

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