Human-Based and Automatic Feature Ideation for Time Series Data: A Comparative Study

dc.contributor.authorSchmidt, Johannaen_US
dc.contributor.authorPiringer, Haralden_US
dc.contributor.authorMühlbacher, Thomasen_US
dc.contributor.authorBernard, Jürgenen_US
dc.contributor.editorAngelini, Marcoen_US
dc.contributor.editorEl-Assady, Mennatallahen_US
dc.date.accessioned2023-06-10T06:09:08Z
dc.date.available2023-06-10T06:09:08Z
dc.date.issued2023
dc.description.abstractFeature ideation is a crucial early step in the feature extraction process, where new features are extracted from raw data. For phenomena existing in time series data, this often includes the ideation of statistical parameters, representations of trends and periodicity, or other geometrical and shape-based characteristics. The strengths of automatic feature ideation methods are their generalizability, applicability, and robustness across cases, whereas human-based feature ideation is most useful in uncharted real-world applications, where incorporating domain knowledge is key. Naturally, both types of methods have proven their right to exist. The motivation for this work is our observation that for time series data, surprisingly few human-based feature ideation approaches exist. In this work, we discuss requirements for human-based feature ideation for VA applications and outline a set of characteristics to assess the goodness of feature sets. Ultimately, we present the results of a comparative study of humanbased and automated feature ideation methods, for time series data in a real-world Industry 4.0 setting. One of our results and discussion items is a call to arms for more human-based feature ideation approaches.en_US
dc.description.sectionheadersPatterns and Multidimensional Projections
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)
dc.identifier.doi10.2312/eurova.20231089
dc.identifier.isbn978-3-03868-222-6
dc.identifier.issn2664-4487
dc.identifier.pages7-12
dc.identifier.pages6 pages
dc.identifier.urihttps://doi.org/10.2312/eurova.20231089
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurova20231089
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleHuman-Based and Automatic Feature Ideation for Time Series Data: A Comparative Studyen_US
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