ub.xmlui.mirage2.page-structure.muninLogoub.xmlui.mirage2.page-structure.openResearchArchiveLogo
    • EnglishEnglish
    • norsknorsk
  • Velg spraaknorsk 
    • EnglishEnglish
    • norsknorsk
  • Administrasjon/UB
Vis innførsel 
  •   Hjem
  • Fakultet for naturvitenskap og teknologi
  • Institutt for informatikk
  • Artikler, rapporter og annet (informatikk)
  • Vis innførsel
  •   Hjem
  • Fakultet for naturvitenskap og teknologi
  • Institutt for informatikk
  • Artikler, rapporter og annet (informatikk)
  • Vis innførsel
JavaScript is disabled for your browser. Some features of this site may not work without it.

Efficient quantile tracking using an oracle

Permanent lenke
https://hdl.handle.net/10037/26509
DOI
https://doi.org/10.1007/s10489-022-03489-1
Thumbnail
Åpne
article.pdf (1.944Mb)
Publisert versjon (PDF)
Dato
2022-04-14
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Forfatter
Hammer, Hugo Lewi; Yazidi, Anis; Riegler, Michael; Rue, Håvard
Sammendrag
Concept drift is a well-known issue that arises when working with data streams. In this paper, we present a procedure that allows a quantile tracking procedure to cope with concept drift. We suggest using expected quantile loss, a popular loss function in quantile regression, to monitor the quantile tracking error, which, in turn, is used to efficiently adapt to concept drift. The suggested procedures adapt efficiently to concept drift, and the tracking performance is close to theoretically optimal. The procedures were further applied to three real-life streaming data sets related to Twitter event detection, activity recognition, and stock trading. The results show that the procedures are efficient at adapting to concept drift, thereby documenting the real-world applicability of the procedures. We further used asymptotic theory from statistics to show the appealing theoretical property that, if the data stream distribution is stationary over time, the procedures converge to the true quantile.
Forlag
Springer Nature
Sitering
Hammer, Yazidi, Riegler, Rue. Efficient quantile tracking using an oracle. Applied intelligence (Boston). 2022
Metadata
Vis full innførsel
Samlinger
  • Artikler, rapporter og annet (informatikk) [481]
Copyright 2022 The Author(s)

Bla

Bla i hele MuninEnheter og samlingerForfatterlisteTittelDatoBla i denne samlingenForfatterlisteTittelDato
Logg inn

Statistikk

Antall visninger
UiT

Munin bygger på DSpace

UiT Norges Arktiske Universitet
Universitetsbiblioteket
uit.no/ub - munin@ub.uit.no

Tilgjengelighetserklæring