用扩散模型提升污水系统预测精度，抗极端天气能力强。
Diffusion-based Time Series Forecasting for Sewerage Systems
- 基于扩散模型处理多变量时序数据，捕捉复杂环境信号关联
- 在真实数据上验证，极端天气下仍保持高预测准确率
- 结合分位数校准技术，确保预测区间统计可靠性
我们提出一种新型深度学习方法，利用生成式人工智能提升污水系统中上下文感知预测的准确性。通过构建处理多变量时间序列数据的扩散模型，系统能够有效捕捉多种环境信号间的复杂相关性，即使在极端天气条件下也能实现稳健预测。为进一步增强模型可靠性，我们采用专为概率性时间序列设计的置信推断技术对预测结果进行校准，确保预测区间在指定置信水平下覆盖真实目标值。在真实污水系统数据上的实证测试表明，该模型具备出色的上下文预测能力，在恶劣天气条件下依然保持高精度。
原文摘要 · Abstract (English)
We introduce a novel deep learning approach that harnesses the power of generative artificial intelligence to enhance the accuracy of contextual forecasting in sewerage systems. By developing a diffusion-based model that processes multivariate time series data, our system excels at capturing complex correlations across diverse environmental signals, enabling robust predictions even during extreme weather events. To strengthen the model's reliability, we further calibrate its predictions with a conformal inference technique, tailored for probabilistic time series data, ensuring that the resulting prediction intervals are statistically reliable and cover the true target values with a desired confidence level. Our empirical tests on real sewerage system data confirm the model's exceptional capability to deliver reliable contextual predictions, maintaining accuracy even under severe weather conditions.
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