A unifying framework for detecting outliers and change points from non-stationary time series data

@inproceedings{Yamanishi2002AUF,
  title={A unifying framework for detecting outliers and change points from non-stationary time series data},
  author={Kenji Yamanishi and Jun'ichi Takeuchi},
  booktitle={KDD},
  year={2002}
}
We are concerned with the issues of outlier detection and change point detection from a data stream. In the area of data mining, there have been increased interest in these issues since the former is related to fraud detection, rare event discovery, etc., while the latter is related to event/trend by change detection, activity monitoring, etc. Specifically, it is important to consider the situation where the data source is non-stationary, since the nature of data source may change over time in… CONTINUE READING

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J.Takeuchi, G.Williams, and P.Milne, On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms, in Proe. of KDD2000

  • K. Yamanishi
  • ACM Press,
  • 2000
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