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Leave-one-out Kernel Density Estimates for Outlier Detection

Author

Listed:
  • Sevvandi Kandanaarachchi
  • Rob J Hyndman

Abstract

This paper introduces lookout, a new approach to detect outliers using leave-one-out kernel density estimates and extreme value theory. Outlier detection methods that use kernel density estimates generally employ a user defined parameter to determine the bandwidth. Lookout uses persistent homology to construct a bandwidth suitable for outlier detection without any user input. We demonstrate the effectiveness of lookout on an extensive data repository by comparing its performance with other outlier detection methods based on extreme value theory. Furthermore, we introduce outlier persistence, a useful concept that explores the birth and the cessation of outliers with changing bandwidth and significance levels. The R package lookout implements this algorithm.

Suggested Citation

  • Sevvandi Kandanaarachchi & Rob J Hyndman, 2021. "Leave-one-out Kernel Density Estimates for Outlier Detection," Monash Econometrics and Business Statistics Working Papers 2/21, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2021-2
    as

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    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/wp02-2021.pdf
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    References listed on IDEAS

    as
    1. Peter Burridge & A. M. Robert Taylor, 2006. "Additive Outlier Detection Via Extreme‐Value Theory," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(5), pages 685-701, September.
    2. Chad M Topaz & Lori Ziegelmeier & Tom Halverson, 2015. "Topological Data Analysis of Biological Aggregation Models," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-26, May.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    anomaly detection; topological data analysis; persistent homology; extreme value theory; peak over thresholds; generalized Pareto distribution;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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