Cristin-resultat-ID: 978769
Sist endret: 2. januar 2013 09:18
NVI-rapporteringsår: 2012
Vitenskapelig artikkel

Causality in Scale Space as an Approach to Change Detection

  • Stein Olav Skrøvseth
  • Fred Godtliebsen og
  • Johan Gustav Bellika


ISSN 1932-6203
e-ISSN 1932-6203
NVI-nivå 1

Om resultatet

Vitenskapelig artikkel
Publiseringsår: 2012
Volum: 7
Hefte: 12
Sider: e52253
Open Access


Scopus-ID: 2-s2.0-84871652600
Isi-ID: 000312829100036

Beskrivelse Beskrivelse


Causality in Scale Space as an Approach to Change Detection


Kernel density estimation and kernel regression are useful ways to visualize and assess the structure of data. Using these techniques we define a temporal scale space as the vector space spanned by bandwidth and a temporal variable. In this space significance regions that reflect a significant derivative in the kernel smooth similar to those of SiZer (Significant Zero-crossings of derivatives) are indicated. Significance regions are established by hypothesis tests for significant gradient at every point in scale space. Causality is imposed onto the space by restricting to kernels with left-bounded or finite support and shifting kernels forward. We show that these adjustments to the methodology enable early detection of changes in time series constituting live surveillance systems of either count data or unevenly sampled measurements. Warning delays are comparable to standard techniques though comparison shows that other techniques may be better suited for single-scale problems. Our method reliably detects change points even with little to no knowledge about the relevant scale of the problem. Hence the technique will be applicable for a large variety of sources without tailoring. Furthermore this technique enables us to obtain a retrospective reliable interval estimate of the time of a change point rather than a point estimate. We apply the technique to disease outbreak detection based on laboratory confirmed cases for pertussis and influenza as well as blood glucose concentration obtained from patients with diabetes type 1.


Stein Olav Skrøvseth

  • Tilknyttet:
    ved Nasjonalt senter for e-helseforskning ved Universitetssykehuset Nord-Norge HF

Fred Godtliebsen

  • Tilknyttet:
    ved Institutt for matematikk og statistikk ved UiT Norges arktiske universitet

Johan Gustav Bellika

  • Tilknyttet:
    ved Institutt for informatikk ved UiT Norges arktiske universitet
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