Exploiting self-similarity for change detection

  title={Exploiting self-similarity for change detection},
  author={Giacomo Boracchi and Manuel Roveri},
  journal={2014 International Joint Conference on Neural Networks (IJCNN)},
Time-series data are often characterized by a large degree of self-similarity, which arises in application domains featuring periodicity or seasonality. While self-similarity has shown to be an effective prior for modeling real data in the signal and image-processing literature, it has received much less attention in time-series literature, where only few works leveraging the self-similarity for anomaly detection have been presented. Here we introduce a novel change-detection test to detect… CONTINUE READING
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