Outlier Detection from Non-Smooth Sensor Data

Timo Huuhtanen, Henrik Ambos, Alex Jung

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Abstract

Outlier detection is usually based on smooth assumption of the data. Most existing approaches for outlier detection from spatial sensor data assume the data to be a smooth function of the location. Spatial discontinuities in the data, such as arising from shadows in photovoltaic (PV) systems, may cause outlier detection methods based on the spatial smoothness assumption to fail. In this paper, we propose novel approaches for outlier detection of non-smooth spatial data. The methods
are evaluated by numerical experiments involving PV panel measurements as well as synthetic data.
Original languageEnglish
Title of host publicationEUSIPCO 2019 - 27th European Signal Processing Conference
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)9789082797039
DOIs
Publication statusPublished - 5 Sep 2019
MoE publication typeA4 Article in a conference publication
EventEuropean Signal Processing Conference - Coruna, Spain
Duration: 2 Sep 20196 Sep 2019

Publication series

NameEuropean Signal Processing Conference
PublisherIEEE
ISSN (Print)2219-5491

Conference

ConferenceEuropean Signal Processing Conference
Abbreviated titleEUSIPCO
CountrySpain
CityCoruna
Period02/09/201906/09/2019

Keywords

  • outlier detection
  • spatial signals

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  • Cite this

    Huuhtanen, T., Ambos, H., & Jung, A. (2019). Outlier Detection from Non-Smooth Sensor Data. In EUSIPCO 2019 - 27th European Signal Processing Conference (pp. 1-5). (European Signal Processing Conference). IEEE. https://doi.org/10.23919/EUSIPCO.2019.8903061