Anomaly Subsequence Detection with Dynamic Local Density for Time Series

@article{Zhang2019AnomalySD,
  title={Anomaly Subsequence Detection with Dynamic Local Density for Time Series},
  author={Chun Kai Zhang and Yingyang Chen and Ao Yin},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.00701}
}
Anomaly subsequence detection is to detect inconsistent data, which always contains important information, among time series. Due to the high dimensionality of the time series, traditional anomaly detection often requires a large time overhead; furthermore, even if the dimensionality reduction techniques can improve the efficiency, they will lose some information and suffer from time drift and parameter tuning. In this paper, we propose a new anomaly subsequence detection with Dynamic Local… 
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Hierarchical Multiresolution Representation of Streaming Time Series

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