Current time-series forecasting problems use short-term weather attributes as exogenous inputs. However, in specific time-series forecasting solutions (e.g., demand prediction in the supply chain), seasonal climate predictions are crucial to improve its resilience. Representing mid to long-term seasonal climate forecasts is challenging as seasonal climate predictions are uncertain, and encoding spatio-temporal relationship of climate forecasts with demand is complex. We propose a novel modeling framework that efficiently encodes seasonal climate predictions to provide robust and reliable time-series forecasting for supply chain functions. The encoding framework enables effective learning of latent representations -- be it uncertain seasonal climate prediction or other time-series data (e.g., buyer patterns) -- via a modular neural network architecture. Our extensive experiments indicate that learning such representations to model seasonal climate forecast results in an error reduction of approximately 13\% to 17\% across multiple real-world data sets compared to existing demand forecasting methods.

翻译：当前时间序列预测问题常将短期天气属性作为外生输入。然而,在特定时间序列预测解决方案(如供应链中的需求预测)中,季节性气候预测对提升其鲁棒性至关重要。表征中长期季节性气候预测颇具挑战,原因在于季节性气候预测具有不确定性,且气候预测与需求之间的时空关系编码复杂。我们提出一种新颖的建模框架,能够高效编码季节性气候预测,为供应链功能提供稳健可靠的时间序列预测。该编码框架通过模块化神经网络架构,有效学习潜在表征——既包括不确定的季节性气候预测,也涵盖其他时间序列数据(如购买者模式)。大量实验表明,与现有需求预测方法相比,利用此类表征建模季节性气候预测,在多个真实数据集上可实现约13%至17%的误差降低。