DCT-Based Channel Attention for Multivariate Time Series Classification
<p dir="ltr">This article introduces a novel DCT-based channel attention (DCA) mechanism for time series classification (TSC) using convolutional neural networks (CNNs). Traditional squeeze-and-excitation (SE) mechanisms rely on global average pooling to model channel-wise interdepen...
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2025
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| _version_ | 1864513531583397888 |
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| author | Amine Haboub (22392097) |
| author2 | Hamza Baali (14603380) Abdesselam Bouzerdoum (17900021) |
| author2_role | author author |
| author_facet | Amine Haboub (22392097) Hamza Baali (14603380) Abdesselam Bouzerdoum (17900021) |
| author_role | author |
| dc.creator.none.fl_str_mv | Amine Haboub (22392097) Hamza Baali (14603380) Abdesselam Bouzerdoum (17900021) |
| dc.date.none.fl_str_mv | 2025-07-25T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/ojcs.2025.3586682 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/DCT-Based_Channel_Attention_for_Multivariate_Time_Series_Classification/30860261 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Machine learning Channel attention convolutional neural networks deep learning discrete cosine transform time series classification Computational modeling Feature extraction Vectors Training Standards Adaptation models Accuracy |
| dc.title.none.fl_str_mv | DCT-Based Channel Attention for Multivariate Time Series Classification |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">This article introduces a novel DCT-based channel attention (DCA) mechanism for time series classification (TSC) using convolutional neural networks (CNNs). Traditional squeeze-and-excitation (SE) mechanisms rely on global average pooling to model channel-wise interdependencies, which may oversimplify complex temporal dynamics. The proposed DCA model leverages discrete cosine transform (DCT) coefficients to incorporate frequency-domain information, capturing a broader spectrum of temporal features. Two selection criteria are employed to identify the most informative DCT coefficients for constructing the attention map. The first criterion utilizes the lowest frequency coefficients, whereas the second criterion selects the coefficients exhibiting the highest energy to construct the attention map. Comprehensive experiments on twelve diverse TSC datasets demonstrate that DCA consistently outperforms state-of-the-art attention mechanisms, achieving an average improvement of 2.2% in classification accuracy.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of the Computer Society<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojcs.2025.3586682" target="_blank">https://dx.doi.org/10.1109/ojcs.2025.3586682</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a9375749693e86cc34335ddcad7e2b51 |
| identifier_str_mv | 10.1109/ojcs.2025.3586682 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30860261 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | DCT-Based Channel Attention for Multivariate Time Series ClassificationAmine Haboub (22392097)Hamza Baali (14603380)Abdesselam Bouzerdoum (17900021)Information and computing sciencesArtificial intelligenceMachine learningChannel attentionconvolutional neural networksdeep learningdiscrete cosine transformtime series classificationComputational modelingFeature extractionVectorsTrainingStandardsAdaptation modelsAccuracy<p dir="ltr">This article introduces a novel DCT-based channel attention (DCA) mechanism for time series classification (TSC) using convolutional neural networks (CNNs). Traditional squeeze-and-excitation (SE) mechanisms rely on global average pooling to model channel-wise interdependencies, which may oversimplify complex temporal dynamics. The proposed DCA model leverages discrete cosine transform (DCT) coefficients to incorporate frequency-domain information, capturing a broader spectrum of temporal features. Two selection criteria are employed to identify the most informative DCT coefficients for constructing the attention map. The first criterion utilizes the lowest frequency coefficients, whereas the second criterion selects the coefficients exhibiting the highest energy to construct the attention map. Comprehensive experiments on twelve diverse TSC datasets demonstrate that DCA consistently outperforms state-of-the-art attention mechanisms, achieving an average improvement of 2.2% in classification accuracy.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of the Computer Society<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojcs.2025.3586682" target="_blank">https://dx.doi.org/10.1109/ojcs.2025.3586682</a></p>2025-07-25T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/ojcs.2025.3586682https://figshare.com/articles/journal_contribution/DCT-Based_Channel_Attention_for_Multivariate_Time_Series_Classification/30860261CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/308602612025-07-25T12:00:00Z |
| spellingShingle | DCT-Based Channel Attention for Multivariate Time Series Classification Amine Haboub (22392097) Information and computing sciences Artificial intelligence Machine learning Channel attention convolutional neural networks deep learning discrete cosine transform time series classification Computational modeling Feature extraction Vectors Training Standards Adaptation models Accuracy |
| status_str | publishedVersion |
| title | DCT-Based Channel Attention for Multivariate Time Series Classification |
| title_full | DCT-Based Channel Attention for Multivariate Time Series Classification |
| title_fullStr | DCT-Based Channel Attention for Multivariate Time Series Classification |
| title_full_unstemmed | DCT-Based Channel Attention for Multivariate Time Series Classification |
| title_short | DCT-Based Channel Attention for Multivariate Time Series Classification |
| title_sort | DCT-Based Channel Attention for Multivariate Time Series Classification |
| topic | Information and computing sciences Artificial intelligence Machine learning Channel attention convolutional neural networks deep learning discrete cosine transform time series classification Computational modeling Feature extraction Vectors Training Standards Adaptation models Accuracy |