A kernel based multi-resolution time series analysis for screening deficiencies in paper production

Authors:
Marcus Ejnarsson;Carl Magnus Nilsson;Antanas Verikas
Affiliations:
Intelligent Systems Laboratory, Halmstad University, Halmstad, Sweden;Intelligent Systems Laboratory, Halmstad University, Halmstad, Sweden;Intelligent Systems Laboratory, Halmstad University, Halmstad, Sweden
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 1
Cited 2

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Abstract

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.