融合小波与图注意力的神经微分方程模型，提升能源预测精度与可解释性。
Wavelet-Enhanced Neural ODE and Graph Attention for Interpretable Energy Forecasting
- 结合小波变换与神经微分方程，捕捉多尺度时间动态特征。
- 在7个数据集上优于主流模型，最高误差降低12.3%。
- 通过SHAP分析增强可解释性，适合可持续能源系统决策。
精准预测能源需求与供给对优化可持续能源系统至关重要，但受可再生能源波动性和消费模式动态性挑战。本文提出一种集成连续时间神经常微分方程（Neural ODEs）、图注意力机制、多分辨率小波变换及自适应频率学习的神经框架，以应对时间序列预测难题。模型采用基于四阶龙格-库塔法的鲁棒求解器，结合图注意力机制与残差连接，有效建模结构与时间模式。通过小波特征提取与自适应频率调制，精准捕捉多样化、多尺度的时间动态。在七个不同数据集（ETTh1、ETTh2、ETTm1、ETTm2、Waste、Solar、Hydro）上评估，该架构在多种预测指标上持续优于当前最优基线，验证了其在复杂时间依赖性建模中的鲁棒性。此外，借助SHAP分析提升可解释性，适用于可持续能源应用场景。
原文摘要 · Abstract (English)
Accurate forecasting of energy demand and supply is critical for optimizing sustainable energy systems, yet it is challenged by the variability of renewable sources and dynamic consumption patterns. This paper introduces a neural framework that integrates continuous-time Neural Ordinary Differential Equations (Neural ODEs), graph attention, multi-resolution wavelet transformations, and adaptive learning of frequencies to address the issues of time series prediction. The model employs a robust ODE solver, using the Runge-Kutta method, paired with graph-based attention and residual connections to better understand both structural and temporal patterns. Through wavelet-based feature extraction and adaptive frequency modulation, it adeptly captures and models diverse, multi-scale temporal dynamics. When evaluated across seven diverse datasets: ETTh1, ETTh2, ETTm1, ETTm2 (electricity transformer temperature), and Waste, Solar, and Hydro (renewable energy), this architecture consistently outperforms state-of-the-art baselines in various forecasting metrics, proving its robustness in capturing complex temporal dependencies. Furthermore, the model enhances interpretability through SHAP analysis, making it suitable for sustainable energy applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。