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Time series outlier detection: a new non parametric methodology (washer)

Author

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  • Andrea Venturini

    (Divisione Analisi e ricerca economica territoriale, Banca d'Italia - Sede di Venezia)

Abstract

The production and exploitation of statistical data for a large amount of high frequency time series must allow a timely use of data ensuring a minimum quality standard. This work provides a new outlier detection methodology (washer): efficient for timesaving elaboration and implementation procedures, adaptable for general assumptions and for needing very short time series, reliable and effective as involving robust non parametric test. Some simulations, a case study and a ready-to-use R-language function (washer.AV()) conclude the work

Suggested Citation

  • Andrea Venturini, 2011. "Time series outlier detection: a new non parametric methodology (washer)," Statistica, Department of Statistics, University of Bologna, vol. 71(3), pages 329-344.
    • Handle: RePEc:bot:rivsta:v:71:y:2011:i:3:p:329-344
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    Cited by:

    1. Baetens, J.M. & Van Nieuland, S. & Pauwels, I.S. & De Baets, B. & Mouton, A.M. & Goethals, P.L.M., 2013. "An individual-based model for the migration of pike (Esox lucius) in the river Yser, Belgium," Ecological Modelling, Elsevier, vol. 258(C), pages 40-52.

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