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Instead, we propose to view time series explainability as saliency maps over interpretable parts, leaning on established signal processing methodology on signal decomposition. Specifically, we propose a new method called FLEXtime that uses a bank of bandpass filters to split the time series into frequency bands. Then, we learn the combination of these bands that optimally explains the model\u2019s prediction. Our extensive evaluation shows that, on average, FLEXtime outperforms state-of-the-art explainability methods across a range of datasets. FLEXtime fills an important gap in the current time series explainability methodology and is a valuable tool for a wide range of time series such as EEG and audio. Code is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/theabrusch\/FLEXtime\" ext-link-type=\"uri\">https:\/\/github.com\/theabrusch\/FLEXtime<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/978-3-032-08330-2_12","type":"book-chapter","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T03:11:29Z","timestamp":1760325089000},"page":"243-267","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FLEXtime: Filterbank Learning to\u00a0Explain Time Series"],"prefix":"10.1007","author":[{"given":"Thea","family":"Br\u00fcsch","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kristoffer Knutsen","family":"Wickstr\u00f8m","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mikkel N.","family":"Schmidt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Jenssen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tommy Sonne","family":"Alstr\u00f8m","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Achtibat, R., et al.: From attribution maps to human-understandable explanations through concept relevance propagation. 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