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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2406.05332 |
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| _version_ | 1866917688400412672 |
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| author | Lee, Junghwan Xu, Chen Xie, Yao |
| author_facet | Lee, Junghwan Xu, Chen Xie, Yao |
| contents | We present a conformal prediction method for time series using the Transformer architecture to capture long-memory and long-range dependencies. Specifically, we use the Transformer decoder as a conditional quantile estimator to predict the quantiles of prediction residuals, which are used to estimate the prediction interval. We hypothesize that the Transformer decoder benefits the estimation of the prediction interval by learning temporal dependencies across past prediction residuals. Our comprehensive experiments using simulated and real data empirically demonstrate the superiority of the proposed method compared to the existing state-of-the-art conformal prediction methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05332 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transformer Conformal Prediction for Time Series Lee, Junghwan Xu, Chen Xie, Yao Machine Learning We present a conformal prediction method for time series using the Transformer architecture to capture long-memory and long-range dependencies. Specifically, we use the Transformer decoder as a conditional quantile estimator to predict the quantiles of prediction residuals, which are used to estimate the prediction interval. We hypothesize that the Transformer decoder benefits the estimation of the prediction interval by learning temporal dependencies across past prediction residuals. Our comprehensive experiments using simulated and real data empirically demonstrate the superiority of the proposed method compared to the existing state-of-the-art conformal prediction methods. |
| title | Transformer Conformal Prediction for Time Series |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.05332 |