Time Series Motif Discovery and Anomaly Detection Based on Subseries Join

@inproceedings{Lin2010TimeSM,
  title={Time Series Motif Discovery and Anomaly Detection Based on Subseries Join},
  author={Yi Juain Lin and Michael D. McCool and Ali A. Ghorbani},
  year={2010}
}
Time series are composed of sequences of data items measured at typically uniform intervals. Time series arise frequently in many scientific and engineering applications, including finance, medicine, digital audio, and motion capture. Time series motifs are repeated similar subseries in one or multiple time series data. Time series anomalies are unusual subseries in one or multiple time series data. Finding motifs and anomalies in time series data are closely related problems and are useful in… CONTINUE READING

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