Kernel-based Optimally Weighted Conformal Time-Series Prediction
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912897306722304 |
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| author | Lee, Jonghyeok Xu, Chen Xie, Yao |
| author_facet | Lee, Jonghyeok Xu, Chen Xie, Yao |
| contents | In this work, we present a novel conformal prediction method for time-series, which we call Kernel-based Optimally Weighted Conformal Prediction Intervals (KOWCPI). Specifically, KOWCPI adapts the classic Reweighted Nadaraya-Watson (RNW) estimator for quantile regression on dependent data and learns optimal data-adaptive weights. Theoretically, we tackle the challenge of establishing a conditional coverage guarantee for non-exchangeable data under strong mixing conditions on the non-conformity scores. We demonstrate the superior performance of KOWCPI on real and synthetic time-series data against state-of-the-art methods, where KOWCPI achieves narrower confidence intervals without losing coverage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_16828 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Kernel-based Optimally Weighted Conformal Time-Series Prediction Lee, Jonghyeok Xu, Chen Xie, Yao Machine Learning Statistics Theory In this work, we present a novel conformal prediction method for time-series, which we call Kernel-based Optimally Weighted Conformal Prediction Intervals (KOWCPI). Specifically, KOWCPI adapts the classic Reweighted Nadaraya-Watson (RNW) estimator for quantile regression on dependent data and learns optimal data-adaptive weights. Theoretically, we tackle the challenge of establishing a conditional coverage guarantee for non-exchangeable data under strong mixing conditions on the non-conformity scores. We demonstrate the superior performance of KOWCPI on real and synthetic time-series data against state-of-the-art methods, where KOWCPI achieves narrower confidence intervals without losing coverage. |
| title | Kernel-based Optimally Weighted Conformal Time-Series Prediction |
| topic | Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2405.16828 |