Encoder-decoder deep neural networks have been increasingly studied for multi-horizon time series forecasting, especially in real-world applications. However, to forecast accurately, these sophisticated models typically rely on a large number of time series examples with substantial history. A rapidly growing topic of interest is forecasting time series which lack sufficient historical data -- often referred to as the ``cold start'' problem. In this paper, we introduce a novel yet simple method to address this problem by leveraging graph neural networks (GNNs) as a data augmentation for enhancing the encoder used by such forecasters. These GNN-based features can capture complex inter-series relationships, and their generation process can be optimized end-to-end with the forecasting task. We show that our architecture can use either data-driven or domain knowledge-defined graphs, scaling to incorporate information from multiple very large graphs with millions of nodes. In our target application of demand forecasting for a large e-commerce retailer, we demonstrate on both a small dataset of 100K products and a large dataset with over 2 million products that our method improves overall performance over competitive baseline models. More importantly, we show that it brings substantially more gains to ``cold start'' products such as those newly launched or recently out-of-stock.

翻译：编码器-解码器深度神经网络在多步时间序列预测（尤其是实际应用场景）中受到日益广泛的研究。然而，为实现精准预测，这些复杂模型通常依赖大量具有充分历史数据的时间序列样本。一个快速发展的研究热点是预测缺乏足够历史数据的时间序列——通常被称为"冷启动"问题。本文提出一种新颖且简洁的方法来解决该问题：利用图神经网络（GNN）作为数据增强手段，增强预测模型所使用的编码器。基于GNN的特征能够捕捉序列间复杂关联关系，其生成过程可与预测任务进行端到端联合优化。我们证明该架构既能使用数据驱动图也能使用领域知识定义图，并可扩展至包含数百万节点的多个超大规模图。在针对大型电商零售商的需求预测应用中，我们在包含10万件商品的小规模数据集和超过200万件商品的大规模数据集上验证，该方法相较于竞争基线模型提升了整体性能。更重要的是，该方法为"冷启动"商品（如新品或近期缺货商品）带来了更显著的性能提升。