Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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An Improved Iterative Procedure for Outlier Detection in Time Series

시계열 이상치 탐지를 위한 개선된 반복적 절차

  • Bui, Anh Tuan (Department of Industrial and Management Engineering, Pohang University of Science and Technology) ;
  • Jun, Chi-Hyuck (Department of Industrial and Management Engineering, Pohang University of Science and Technology)
  • ;
  • 전치혁 (포항공과대학교 산업경영공학과)
  • Received : 2011.07.14
  • Accepted : 2011.11.09
  • Published : 2012.03.01
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Abstract

We address some potential problems with the existing procedures of outlier detection in time series. Also we propose modifications in estimating model parameters and outlier effects in order to reduce the number of tests and to increase the detection accuracy. Experiments with some artificial data sets show that the proposed procedure significantly reduces the number of tests and enhances the accuracy of estimated parameters as well as the detection power.

Keywords

References

  1. Chang, I. (1982), Outliers in Time Series, Unpublished Ph.D. thesis, University of Wisconsin-Madison, Dept. of Statistic.
  2. Charles, A. and Darne, O. (2005), Outliers and GARCH models in financial data, Economic Letters, 86, 347-352. https://doi.org/10.1016/j.econlet.2004.07.019
  3. Chen, C. and Liu, L.-M. (1993), Joint estimation of model parameters and outlier effects in time series, Journal of the American Statistical Association, 88, 284-297.
  4. Fox, A. J. (1972), Outliers in time series, Journal of the Royal Statistical Society, Ser. B, 34, 350-363.
  5. Franses, P. H. and Ghijsels, H. (1999), Additive Outliers, GARCH and Forecasting Volatility, International Journal of Forecasting, 15, 1-9. https://doi.org/10.1016/S0169-2070(98)00053-3
  6. Galeano, P., Peña, D., and Tsay, R. (2006), Outlier detection in multivariate time series by projection pursuit, Journal of the American Statistical Association, 101, 654-669. https://doi.org/10.1198/016214505000001131
  7. Sanchez, M. J. and Pena, D. (2003), The identification of multiple outliers in ARIMA models, Communications in Statistics : Theory and Methods, 32, 1265-1287.
  8. SCA (Scientific Computing Associates) (1994), The SCA statistical system reference manual, version 4.3. Oak Brook, SCA.
  9. Tsay, R. S. (1988), Outliers, level shifts, and variance changes in time series, Journal of Forecasting, 7, 1-20. https://doi.org/10.1002/for.3980070102
  10. Wei, W. W. S. (2006), Time Series Analysis : Univariate and Multivariate Methods, second ed. New York, Addison-Wesley.

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