SCHEDA: Lightweight euclidean-like heuristics for anomaly detection in periodic time series

Author:

Guigou Fabio,Collet Pierre,Parrend PierreORCID

Funder

French ANRT

French Banque Publique d’Investissement

Publisher

Elsevier BV

Subject

Software

Reference41 articles.

1. Short-term load forecasting, profile identification, and customer segmentation: a methodology based on periodic time series;Espinoza;IEEE Trans. Power Syst.,2005

2. D.T. Shipmon, J.M. Gurevitch, P.M. Piselli, S.T. Edwards, Time series anomaly detection; detection of anomalous drops with limited features and sparse examples in noisy highly periodic data, arXiv preprint arXiv:1708.03665.

3. Evaluating real-time anomaly detection algorithms–the numenta anomaly benchmark;Lavin,2015

4. Assumption-free anomaly detection in time series;Wei,2005

5. Anomaly detection: A survey;Chandola;ACM Comput. Surv.,2009

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