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An Improved k-Nearest Neighbours Method for Traffic Time Series Imputation
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.ORCID iD: 0000-0001-5824-425X
University of Jinan, CHI.
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
Blekinge Institute of Technology, Faculty of Computing, Department of Technology and Aesthetics.
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2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Intelligent transportation systems (ITS) are becoming more and more effective, benefiting from big data. Despite this, missing data is a problem that prevents many prediction algorithms in ITS from working effectively. Much work has been done to impute those missing data. Among different imputation methods, k-nearest neighbours (kNN) has shown excellent accuracy and efficiency. However, the general kNN is designed for matrix instead of time series so it lacks the usage of time series characteristics such as windows and weights that are gap-sensitive. This work introduces gap-sensitive windowed kNN (GSW-kNN) imputation for time series. The results show that GSW-kNN is 34% more accurate than benchmarking methods, and it is still robust even if the missing ratio increases to 90%.

Place, publisher, year, edition, pages
IEEE, 2017. Vol. 10
Keywords [en]
Traffic Time Series, Gap-Sensitive Windowed k-Nearest Neighbours (GSW-kNN), Missing Data Imputation
National Category
Transport Systems and Logistics Computer Sciences
Identifiers
URN: urn:nbn:se:bth-15209ISI: 000427816107081ISBN: 978-1-5386-3524-7 OAI: oai:DiVA.org:bth-15209DiVA, id: diva2:1145422
Conference
Chinese Automation Congress (CAC), Jinan
Available from: 2017-09-28 Created: 2017-09-28 Last updated: 2018-04-26Bibliographically approved

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Sun, BinWei, ChengWei, WenPrashant, GoswamiGuohua, Bai
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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • Other locale
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Output format
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