Large-scale unusual time series detection

Rob J. Hyndman, Earo Wang, Nikolay Laptev

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

52 Citations (Scopus)

Abstract

It is becoming increasingly common for organizations to collect very large amounts of data over time, and to need to detect unusual or anomalous time series. For example, Yahoo has banks of mail servers that are monitored over time. Many measurements on server performance are collected every hour for each of thousands of servers. We wish to identify servers that are behaving unusually. We compute a vector of features on each time series, measuring characteristics of the series. The features may include lag correlation, strength of seasonality, spectral entropy, etc. Then we use a principal component decomposition on the features, and use various bivariate outlier detection methods applied to the first two principal components. This enables the most unusual series, based on their feature vectors, to be identified. The bivariate outlier detection methods used are based on highest density regions and α-hulls.

Original languageEnglish
Title of host publicationProceedings - 15th IEEE International Conference on Data Mining Workshop
Subtitle of host publicationICDMW 2015
EditorsPeng Cui, Jennifer Dry, Charu Aggarwal, Zhi-Hua Zhou, Alexander Tuzhilin, Hui Xiong, Xindong Wu
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1616-1619
Number of pages4
ISBN (Print)9781467384926
DOIs
Publication statusPublished - 2015
EventIEEE International Conference on Data Mining Workshops 2015 - Bally's Atlantic City Hotel, Atlantic City, United States of America
Duration: 14 Nov 201517 Nov 2015
Conference number: 15th
https://icdm2015.stonybrook.edu/

Conference

ConferenceIEEE International Conference on Data Mining Workshops 2015
Abbreviated titleICDMW 2015
CountryUnited States of America
CityAtlantic City
Period14/11/1517/11/15
Internet address

Keywords

  • Feature Space
  • Multivariate Anomaly Detection
  • Outliers
  • Time Series Characteristics

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