Discriminant analysis based on binary time series

Yuichi Goto*, Masanobu Taniguchi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Binary time series can be derived from an underlying latent process. In this paper, we consider an ellipsoidal alpha mixing strictly stationary process and discuss the discriminant analysis and propose a classification method based on binary time series. Assume that the observations are generated by time series which belongs to one of two categories described by different spectra. We propose a method to classify into the correct category with high probability. First, we will show that the misclassification probability tends to zero when the number of observation tends to infinity, that is, the consistency of our discrimination method. Further, we evaluate the asymptotic misclassification probability when the two categories are contiguous. Finally, we show that our classification method based on binary time series has good robustness properties when the process is contaminated by an outlier, that is, our classification method is insensitive to the outlier. However, the classical method based on smoothed periodogram is sensitive to outliers. We also deal with a practical case where the two categories are estimated from the training samples. For an electrocardiogram data set, we examine the robustness of our method when observations are contaminated with an outlier.

Original languageEnglish
Pages (from-to)569-595
Number of pages27
Issue number5
Publication statusPublished - 2020 Jul 1


  • Binary time series
  • Discriminant analysis
  • Misclassification probability
  • Robustness
  • Spectral density
  • Stationary process

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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