Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.01343 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916112772366336 |
|---|---|
| author | Huang, Qi Chen, Wei Bäck, Thomas van Stein, Niki |
| author_facet | Huang, Qi Chen, Wei Bäck, Thomas van Stein, Niki |
| contents | In this work, we propose a model-agnostic instance-based post-hoc explainability method for time series classification. The proposed algorithm, namely Time-CF, leverages shapelets and TimeGAN to provide counterfactual explanations for arbitrary time series classifiers. We validate the proposed method on several real-world univariate time series classification tasks from the UCR Time Series Archive. The results indicate that the counterfactual instances generated by Time-CF when compared to state-of-the-art methods, demonstrate better performance in terms of four explainability metrics: closeness, sensibility, plausibility, and sparsity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01343 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification Huang, Qi Chen, Wei Bäck, Thomas van Stein, Niki Machine Learning In this work, we propose a model-agnostic instance-based post-hoc explainability method for time series classification. The proposed algorithm, namely Time-CF, leverages shapelets and TimeGAN to provide counterfactual explanations for arbitrary time series classifiers. We validate the proposed method on several real-world univariate time series classification tasks from the UCR Time Series Archive. The results indicate that the counterfactual instances generated by Time-CF when compared to state-of-the-art methods, demonstrate better performance in terms of four explainability metrics: closeness, sensibility, plausibility, and sparsity. |
| title | Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.01343 |