用扩散模型生成图信号，提升股票价格概率预测能力
Graph Signal Generative Diffusion Models
- 设计U形图神经网络，通过跳跃连接和零填充池化保持图结构
- 在股票价格预测中实现不确定性建模，有效捕捉极端波动事件
- 适合需要概率性预测的金融时序分析场景
我们提出基于去噪扩散过程的U形编码器-解码器图神经网络（U-GNN），用于随机图信号生成。该架构通过编码器与解码器路径间的跳跃连接，在不同分辨率下学习节点特征，类似于图像生成中的卷积U-Net。其关键创新在于一种利用零填充的池化操作，避免了任意图粗化，同时在顶层叠加图卷积以捕捉局部依赖关系。该方法使深层网络仍能保持对原始图结构的卷积特性，从而学习采样节点的特征嵌入。应用于股票价格预测——在该任务中确定性预测难以捕捉不确定性与尾部事件——我们验证了扩散模型在概率预测方面的有效性。
原文摘要 · Abstract (English)
We introduce U-shaped encoder-decoder graph neural networks (U-GNNs) for stochastic graph signal generation using denoising diffusion processes. The architecture learns node features at different resolutions with skip connections between the encoder and decoder paths, analogous to the convolutional U-Net for image generation. The U-GNN is prominent for a pooling operation that leverages zero-padding and avoids arbitrary graph coarsening, with graph convolutions layered on top to capture local dependencies. This technique permits learning feature embeddings for sampled nodes at deeper levels of the architecture that remain convolutional with respect to the original graph. Applied to stock price prediction -- where deterministic forecasts struggle to capture uncertainties and tail events that are paramount -- we demonstrate the effectiveness of the diffusion model in probabilistic forecasting of stock prices.
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