Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Post-hoc Explainability for Time Series Classification: Towards a Signal Processing Perspective
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.ORCID iD: 0000-0003-0995-9835
School of Electrical Engineering, Switzerland.
Stockholm University, Sweden.
Stockholm University, Sweden.
Show others and affiliations
2022 (English)In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 39, no 4, p. 119-129Article in journal (Refereed) Published
Abstract [en]

Time series data correspond to observations of phenomena that are recorded over time [1]. Such data are encountered regularly in a wide range of applications, such as speech and music recognition, monitoring health and medical diagnosis, financial analysis, motion tracking, and shape identification, to name a few. With such a diversity of applications and the large variations in their characteristics, time series classification is a complex and challenging task. One of the fundamental steps in the design of time series classifiers is that of defining or constructing the discriminant features that help differentiate between classes. This is typically achieved by designing novel representation techniques [2] that transform the raw time series data to a new data domain, where subsequently a classifier is trained on the transformed data, such as one-nearest neighbors [3] or random forests [4]. In recent time series classification approaches, deep neural network models have been employed that are able to jointly learn a representation of time series and perform classification [5]. In many of these sophisticated approaches, the discriminant features tend to be complicated to analyze and interpret, given the high degree of nonlinearity.

Place, publisher, year, edition, pages
2022. Vol. 39, no 4, p. 119-129
National Category
Signal Processing Computer Sciences
Identifiers
URN: urn:nbn:se:ri:diva-58800DOI: 10.1109/MSP.2022.3155955Scopus ID: 2-s2.0-85133840717OAI: oai:DiVA.org:ri-58800DiVA, id: diva2:1643492
Available from: 2022-03-09 Created: 2022-03-09 Last updated: 2023-06-07Bibliographically approved

Open Access in DiVA

fulltext(7747 kB)793 downloads
File information
File name FULLTEXT01.pdfFile size 7747 kBChecksum SHA-512
2c6516273b7e22b0609353313fc7da871f20cce635139f6ebc8fca2f1d50b255b49db2bd427c12faf4d39caec27ccd71e5f5c6a5666949190d08ee946974b190
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Mochaourab, Rami

Search in DiVA

By author/editor
Mochaourab, Rami
By organisation
Industrial Systems
In the same journal
IEEE signal processing magazine (Print)
Signal ProcessingComputer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 793 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 149 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf