Methodology for an Offline Outlier Detection in a Dynamic Measurement Data Set

Osman Aksu, Bastian Eisenmann, Michael Schmid, Florian Bierwirth, Mišel Radosavac, Hans Georg Herzog

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

Abstract

The data acquisition process translates physical magnitudes into discrete, sampled data sets. Theoretically, this process executes undisturbed and without delay. But especially if accurate statistic distributions or extremal values are desired, outliers caused by physical errors in the measurement process falsify the result. Conventional approaches have difficulties with detecting outliers in dynamic and distorted data sets. This work shows two methods to classify and reliably improve these aspects. The first approach involves multiplying two factors to yield a quality factor determining outliers. In contrast, the second approach determines outlier status by identifying specific points through the addition of multiple factors, rather than by multiplying two factors. The combination of both methods leads to a more effective detection process, offering a time-efficient and robust technique for pinpointing outlying sampling points in transient measurement data sets.

Original languageEnglish
Title of host publication2024 IEEE 100th Vehicular Technology Conference, VTC 2024-Fall - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331517786
DOIs
StatePublished - 2024
Event100th IEEE Vehicular Technology Conference, VTC 2024-Fall - Washington, United States
Duration: 7 Oct 202410 Oct 2024

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252

Conference

Conference100th IEEE Vehicular Technology Conference, VTC 2024-Fall
Country/TerritoryUnited States
CityWashington
Period7/10/2410/10/24

Keywords

  • digital filter
  • distorted data
  • dynamic measurement data
  • outlier detection
  • outlier filtering

Fingerprint

Dive into the research topics of 'Methodology for an Offline Outlier Detection in a Dynamic Measurement Data Set'. Together they form a unique fingerprint.

Cite this