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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2411.05841 |
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| _version_ | 1866909562732281856 |
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| author | Brüsch, Thea Wickstrøm, Kristoffer K. Schmidt, Mikkel N. Jenssen, Robert Alstrøm, Tommy S. |
| author_facet | Brüsch, Thea Wickstrøm, Kristoffer K. Schmidt, Mikkel N. Jenssen, Robert Alstrøm, Tommy S. |
| contents | State-of-the-art methods for explaining predictions from time series involve learning an instance-wise saliency mask for each time step; however, many types of time series are difficult to interpret in the time domain, due to the inherently complex nature of the data. 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's 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 https://github.com/theabrusch/FLEXtime. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05841 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FLEXtime: Filterbank learning to explain time series Brüsch, Thea Wickstrøm, Kristoffer K. Schmidt, Mikkel N. Jenssen, Robert Alstrøm, Tommy S. Machine Learning State-of-the-art methods for explaining predictions from time series involve learning an instance-wise saliency mask for each time step; however, many types of time series are difficult to interpret in the time domain, due to the inherently complex nature of the data. 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's 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 https://github.com/theabrusch/FLEXtime. |
| title | FLEXtime: Filterbank learning to explain time series |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.05841 |