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Anomaly Detection in Streaming Time Series Based on Bounding Boxes

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Similarity Search and Applications (SISAP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8821))

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Abstract

Anomaly detection in time series has been studied extensively by the scientific community utilizing a wide range of applications. One specific technique that obtains very good results is “HOT SAX”, because it only requires a parameter the length of the subsequence, and it does not need a training model for detecting anomalies. However, its disadvantage is that it requires the use of a normalized Euclidean distance, which in turn requires setting a parameter ε to avoid detecting meaningless patterns (noise in the signal). Setting an appropriate ε requires an analysis of the domain of the values from the time series, which implies normalizing all subsequences before performing the detection. We propose an approach for anomaly detection based on bounding boxes, which does not require normalizing the subsequences, thus it does not need to set ε. Thereby, the proposed technique can be used directly for online detection, without any a priori knowledge and using the non-normalized Euclidean distance. Moreover, we show that our algorithm computes less CPU runtime in finding the anomaly than HOT SAX in normalized scenarios.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Chile, Santiago, Chile

    Heider Sanchez & Benjamin Bustos

Authors
  1. Heider Sanchez
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  2. Benjamin Bustos
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of São Paulo, São Carlos, Brazil

    Agma Juci Machado Traina

  2. University of Sao Paulo at Sao Carlos - USP, Av. do Trabalhador Saocarlense 400, 13566-590, Sao Carlos, Brazil

    Caetano Traina Jr.

  3. University of Sal Paulo at Sao Carlos - USP, Av. do Trabalhador Saocarlense 400, 13566-590, Sao Carlos, Brazil

    Robson Leonardo Ferreira Cordeiro

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Sanchez, H., Bustos, B. (2014). Anomaly Detection in Streaming Time Series Based on Bounding Boxes. In: Traina, A.J.M., Traina, C., Cordeiro, R.L.F. (eds) Similarity Search and Applications. SISAP 2014. Lecture Notes in Computer Science, vol 8821. Springer, Cham. https://doi.org/10.1007/978-3-319-11988-5_19

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  • DOI: https://doi.org/10.1007/978-3-319-11988-5_19

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11987-8

  • Online ISBN: 978-3-319-11988-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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