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Univariate Time Series Anomaly Labelling Algorithm

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Machine Learning, Optimization, and Data Science (LOD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12566))

Abstract

Unsupervised anomaly detection in an n-length univariate time series often comes with high risk. Anomaly contextual dependencies limit the application of binary classification methods. Analyzing the statistical features of data may help enrich the context of anomaly detection. This article proposes a quadratic time algorithm for analyzing possible anomalies in the context of unsupervised learning. Detection of possible anomalies uses Median Absolute Deviation on the residual of a univariate time series. Computation of residuals uses robust STL (Seasonal and Trend decomposition using Loess). Experiments on three datasets (Yahoo, NUMENTA NAB and district-heating substation power profiles) show the ability of the algorithm to enrich anomalies by associating labels such as Certainty, Uncertainty, and Probable, with the probable class indicating a need to further process the anomalies.

The work presented in this article is financed by the Swedish Knowledge Foundation (KKS http://www.kks.se/om-oss/in-english/) under grant no Dnr. 20170182 within the project Data Analytics for Fault Detection in District Heating (DAD).

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References

  1. Adikaram, K.L.B., Hussein, M.A., Effenberger, M., Becker, T.: Data transformation technique to improve the outlier detection power of Grubbs’ test for data expected to follow linear relation. J. Appl. Math. 2015, 1–9 (2015)

    Article  MATH  Google Scholar 

  2. Ahmad, S., Lavin, A., Purdy, S., Agha, Z.: Unsupervised real-time anomaly detection for streaming data. Neurocomputing 262, 134–147 (2017)

    Article  Google Scholar 

  3. Blázquez-García, A., Conde, A., Mori, U., Lozano, J.A.: A review on outlier/anomaly detection in time series data. arXiv preprint arXiv:2002.04236 (2020)

  4. Braei, M., Wagner, S.: Anomaly detection in univariate time-series: a survey on the state-of-the-art. arXiv preprint arXiv:2004.00433 (2020)

  5. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 1–58 (2009)

    Article  Google Scholar 

  6. Committee, A.M., et al.: Robust statistics-how not to reject outliers. Part 1. Basic concepts. Analyst 114(12), 1693–1697 (1989)

    Google Scholar 

  7. Freeman, C., Merriman, J., Beaver, I., Mueen, A.: Experimental comparison of online anomaly detection algorithms. In: The Thirty-Second International Flairs Conference (2019)

    Google Scholar 

  8. Grechuk, B., Molyboha, A., Zabarankin, M.: Chebyshev inequalities with law-invariant deviation measures. Prob. Eng. Inf. Sci. 24(1), 145–170 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  9. Hochenbaum, J., Vallis, O.S., Kejariwal, A.: Automatic anomaly detection in the cloud via statistical learning. arXiv preprint arXiv:1704.07706 (2017)

  10. Huang, H.: Rank based anomaly detection algorithms (2013)

    Google Scholar 

  11. Iwata, T., Toyoda, M., Tora, S., Ueda, N.: Anomaly detection with inexact labels. Mach. Learn. 109(8), 1617–1633 (2020). https://doi.org/10.1007/s10994-020-05880-w

    Article  MathSciNet  MATH  Google Scholar 

  12. Knox, E.M., Ng, R.T.: Algorithms for mining distance based outliers in large datasets. In: Proceedings of the International Conference on Very Large Data Bases, pp. 392–403. Citeseer (1998)

    Google Scholar 

  13. Mehrotra, K.G., Mohan, C.K., Huang, H.: Anomaly Detection Principles and Algorithms. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67526-8

    Book  Google Scholar 

  14. Moustakidis, S., et al.: Innovative technologies for district heating and cooling: indeal project. In: Multidisciplinary Digital Publishing Institute Proceedings, vol. 5, p. 1 (2019)

    Google Scholar 

  15. Pearson, R.K., Neuvo, Y., Astola, J., Gabbouj, M.: Generalized Hampel filters. EURASIP J. Adv. Signal Process. 2016(1), 1–18 (2016)

    Article  Google Scholar 

  16. Rao, T.S., Antunes, A.M.C.: Spatio-temporal modelling of temperature time series: a comparative study. In: Time Series Analysis and Applications to Geophysical Systems, pp. 123–150. Springer, Cham (2004). https://doi.org/10.1007/978-3-319-67526-8

  17. Rousseeuw, P.J., Hubert, M.: Robust statistics for outlier detection. Wiley Interdisc. Rev. Data Min. Knowl. Disc. 1(1), 73–79 (2011)

    Article  Google Scholar 

  18. Wen, Q., Gao, J., Song, X., Sun, L., Xu, H., Zhu, S.: RobustSTL: a robust seasonal-trend decomposition algorithm for long time series. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5409–5416 (2019)

    Google Scholar 

  19. Wu, W., He, L., Lin, W.: Local trend inconsistency: a prediction-driven approach to unsupervised anomaly detection in multi-seasonal time series. arXiv preprint arXiv:1908.01146 (2019)

  20. Yahoo: S5 - a labeled anomaly detection dataset, version 1.0(16m). https://webscope.sandbox.yahoo.com/catalog.php?datatype=s%5c&did=70

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Author information

Authors and Affiliations

  1. Univeristy of Boras, 501 90, Boras, Sweden

    Gideon Mbiydzenyuy

Authors
  1. Gideon Mbiydzenyuy
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Corresponding author

Correspondence to Gideon Mbiydzenyuy .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Giorgio Jansen

  5. Almawave, Rome, Italy

    Vincenzo Sciacca

  6. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Mbiydzenyuy, G. (2020). Univariate Time Series Anomaly Labelling Algorithm. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12566. Springer, Cham. https://doi.org/10.1007/978-3-030-64580-9_48

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  • DOI: https://doi.org/10.1007/978-3-030-64580-9_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64579-3

  • Online ISBN: 978-3-030-64580-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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