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}
}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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