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 '02},
  year={2002}
}
  • Kenji Yamanishi, Jun'ichi Takeuchi
  • Published in KDD '02 2002
  • Computer Science
  • We are concerned with the issues of outlier detection and change point detection from a data stream. [...] Key Method In this framework a probabilistic model of the data source is incrementally learned using an on-line discounting learning algorithm, which can track the changing data source adaptively by forgetting the effect of past data gradually.Expand Abstract
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