From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915493368037376 |
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| author | Gao, Wentao Li, Jiuyong Liu, Lin Le, Thuc Duy Chen, Xiongren Du, Xiaojing Liu, Jixue Zhao, Yanchang Chen, Yun |
| author_facet | Gao, Wentao Li, Jiuyong Liu, Lin Le, Thuc Duy Chen, Xiongren Du, Xiaojing Liu, Jixue Zhao, Yanchang Chen, Yun |
| contents | Zero-inflated data pose significant challenges in precipitation forecasting due to the predominance of zeros with sparse non-zero events. To address this, we propose the Zero Inflation Diffusion Framework (ZIDF), which integrates Gaussian perturbation for smoothing zero-inflated distributions, Transformer-based prediction for capturing temporal patterns, and diffusion-based denoising to restore the original data structure. In our experiments, we use observational precipitation data collected from South Australia along with synthetically generated zero-inflated data. Results show that ZIDF demonstrates significant performance improvements over multiple state-of-the-art precipitation forecasting models, achieving up to 56.7\% reduction in MSE and 21.1\% reduction in MAE relative to the baseline Non-stationary Transformer. These findings highlight ZIDF's ability to robustly handle sparse time series data and suggest its potential generalizability to other domains where zero inflation is a key challenge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10501 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction Gao, Wentao Li, Jiuyong Liu, Lin Le, Thuc Duy Chen, Xiongren Du, Xiaojing Liu, Jixue Zhao, Yanchang Chen, Yun Machine Learning Artificial Intelligence Zero-inflated data pose significant challenges in precipitation forecasting due to the predominance of zeros with sparse non-zero events. To address this, we propose the Zero Inflation Diffusion Framework (ZIDF), which integrates Gaussian perturbation for smoothing zero-inflated distributions, Transformer-based prediction for capturing temporal patterns, and diffusion-based denoising to restore the original data structure. In our experiments, we use observational precipitation data collected from South Australia along with synthetically generated zero-inflated data. Results show that ZIDF demonstrates significant performance improvements over multiple state-of-the-art precipitation forecasting models, achieving up to 56.7\% reduction in MSE and 21.1\% reduction in MAE relative to the baseline Non-stationary Transformer. These findings highlight ZIDF's ability to robustly handle sparse time series data and suggest its potential generalizability to other domains where zero inflation is a key challenge. |
| title | From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.10501 |