Publikation:

TS-MULE : Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models

Lade...
Vorschaubild

Dateien

Schlegel_2-vxq3bhqko6i12.pdf
Schlegel_2-vxq3bhqko6i12.pdfGröße: 663.51 KBDownloads: 54

Datum

2022

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

KAMP, Michael, ed. and others. Machine Learning and Principles and Practice of Knowledge Discovery in Databases : International Workshops of ECML PKDD 2021, Proceedings, Part I. Cham: Springer, 2022, pp. 5-14. Communications in Computer and Information Science. 1524. ISBN 978-3-030-93735-5. Available under: doi: 10.1007/978-3-030-93736-2_1

Zusammenfassung

Time series forecasting is a demanding task ranging from weather to failure forecasting with black-box models achieving state-of-the-art performances. However, understanding and debugging are not guaranteed. We propose TS-MULE, a local surrogate model explanation method specialized for time series extending the LIME approach. Our extended LIME works with various ways to segment and perturb the time series data. In our extension, we present six sampling segmentation approaches for time series to improve the quality of surrogate attributions and demonstrate their performances on three deep learning model architectures and three common multivariate time series datasets.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Konferenz

Machine Learning and Principles and Practice of Knowledge Discovery in Databases : International Workshops of ECML PKDD 2021, 13. Sept. 2021 - 17. Sept. 2021
Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690SCHLEGEL, Udo, Duy Lam VO, Daniel A. KEIM, Daniel SEEBACHER, 2022. TS-MULE : Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models. Machine Learning and Principles and Practice of Knowledge Discovery in Databases : International Workshops of ECML PKDD 2021, 13. Sept. 2021 - 17. Sept. 2021. In: KAMP, Michael, ed. and others. Machine Learning and Principles and Practice of Knowledge Discovery in Databases : International Workshops of ECML PKDD 2021, Proceedings, Part I. Cham: Springer, 2022, pp. 5-14. Communications in Computer and Information Science. 1524. ISBN 978-3-030-93735-5. Available under: doi: 10.1007/978-3-030-93736-2_1
BibTex
@inproceedings{Schlegel2022TSMUL-55037,
  year={2022},
  doi={10.1007/978-3-030-93736-2_1},
  title={TS-MULE : Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models},
  number={1524},
  isbn={978-3-030-93735-5},
  publisher={Springer},
  address={Cham},
  series={Communications in Computer and Information Science},
  booktitle={Machine Learning and Principles and Practice of Knowledge Discovery in Databases : International Workshops of ECML PKDD 2021, Proceedings, Part I},
  pages={5--14},
  editor={Kamp, Michael},
  author={Schlegel, Udo and Vo, Duy Lam and Keim, Daniel A. and Seebacher, Daniel}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/55037">
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/55037"/>
    <dc:creator>Vo, Duy Lam</dc:creator>
    <dc:language>eng</dc:language>
    <dcterms:abstract xml:lang="eng">Time series forecasting is a demanding task ranging from weather to failure forecasting with black-box models achieving state-of-the-art performances. However, understanding and debugging are not guaranteed. We propose TS-MULE, a local surrogate model explanation method specialized for time series extending the LIME approach. Our extended LIME works with various ways to segment and perturb the time series data. In our extension, we present six sampling segmentation approaches for time series to improve the quality of surrogate attributions and demonstrate their performances on three deep learning model architectures and three common multivariate time series datasets.</dcterms:abstract>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-09-27T13:05:59Z</dc:date>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:issued>2022</dcterms:issued>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Schlegel, Udo</dc:creator>
    <dc:contributor>Seebacher, Daniel</dc:contributor>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/55037/1/Schlegel_2-vxq3bhqko6i12.pdf"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:title>TS-MULE : Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models</dcterms:title>
    <dc:contributor>Schlegel, Udo</dc:contributor>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/55037/1/Schlegel_2-vxq3bhqko6i12.pdf"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-09-27T13:05:59Z</dcterms:available>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Seebacher, Daniel</dc:creator>
    <dc:contributor>Vo, Duy Lam</dc:contributor>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen