A Kernel Based Multi-resolution Time Series Analysis for Screening Deficiencies in Paper Production

  • Marcus Ejnarsson
  • Carl Magnus Nilsson
  • Antanas Verikas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


This paper is concerned with a multi-resolution tool for analysis of a time series aiming to detect abnormalities in various frequency regions. The task is treated as a kernel based novelty detection applied to a multi-level time series representation obtained from the discrete wavelet transform. Having a priori knowledge that the abnormalities manifest themselves in several frequency regions, a committee of detectors utilizing data dependent aggregation weights is build by combining outputs of detectors operating in those regions.


Discrete Wavelet Transform Detection Accuracy Paper Mill Paper Structure Novelty Detector 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marcus Ejnarsson
    • 1
  • Carl Magnus Nilsson
    • 1
  • Antanas Verikas
    • 1
    • 2
  1. 1.Intelligent Systems LaboratoryHalmstad UniversityHalmstadSweden
  2. 2.Department of Applied ElectronicsKaunas University of TechnologyKaunasLithuania

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