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

Andrea Venturini ()

Statistica, 2011, vol. 71, issue 3, 329-344

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

Date: 2011
References: Add references at CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bot:rivsta:v:71:y:2011:i:3:p:329-344

Access Statistics for this article

Statistica is currently edited by Department of Statistics, University of Bologna

More articles in Statistica from Department of Statistics, University of Bologna Contact information at EDIRC.
Bibliographic data for series maintained by Giovanna Galatà ().