用扩散模型解决降水预测中大量零值问题，提升精度。
From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction
- 结合高斯扰动、Transformer和扩散去噪，处理零值密集的降水数据。
- 相比基线模型，MSE降低56.7%，MAE降低21.1%。
- 适合处理零膨胀的稀疏时间序列，如气象、医疗等场景。
零值密集的数据在降水预测中带来巨大挑战，因零值占主导且非零事件稀疏。为此，我们提出零膨胀扩散框架（ZIDF），融合高斯扰动以平滑零值分布，利用基于Transformer的预测捕捉时间模式，并通过扩散去噪恢复原始数据结构。实验采用南澳大利亚观测降水数据及合成的零值膨胀数据。结果表明，ZIDF在多个先进降水预测模型上表现显著提升，相较基线非平稳Transformer模型，最大实现MSE降低56.7%、MAE降低21.1%。研究验证了该方法在处理稀疏时间序列中的鲁棒性，也暗示其在其他存在零膨胀问题的领域具备广泛适用性。
原文摘要 · Abstract (English)
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.
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