用时间对齐注意力提升电商峰值需求预测准确率
TAT: Temporal-Aligned Transformer for Multi-Horizon Peak Demand Forecasting
- 引入时间对齐注意力机制，利用节假日促销等先验信息
- 在电商数据集上峰值预测准确率提升最高达30%
- 适合需要精准长周期需求预估的供应链管理者
多时域时间序列预测在需求预测中具有广泛应用，如电商平台和实体零售商的供应链管理。准确的需求预测对采购与库存决策至关重要，通常需覆盖未来数周甚至数十周。在高影响促销活动中，需求峰值更难预测，但此类事件对供应链运营和客户购物体验都极为关键。为此，我们提出时空对齐变压器（TAT），一种利用已知上下文变量（如节假日、促销活动）提升预测性能的多时域预测模型。该模型包含编码器与解码器，均嵌入新型时间对齐注意力（TAA）机制，以学习上下文相关的时序对齐关系。我们在某大型电商企业的两个大规模私有数据集上进行广泛实验，结果表明，TAT在峰值需求预测上最多可提升30%准确率，同时整体性能优于或媲美现有先进方法。
原文摘要 · Abstract (English)
Multi-horizon time series forecasting has many practical applications such as demand forecasting. Accurate demand prediction is critical to help make buying and inventory decisions for supply chain management of e-commerce and physical retailers, and such predictions are typically required for future horizons extending tens of weeks. This is especially challenging during high-stake sales events when demand peaks are particularly difficult to predict accurately. However, these events are important not only for managing supply chain operations but also for ensuring a seamless shopping experience for customers. To address this challenge, we propose Temporal-Aligned Transformer (TAT), a multi-horizon forecaster leveraging apriori-known context variables such as holiday and promotion events information for improving predictive performance. Our model consists of an encoder and decoder, both embedded with a novel Temporal Alignment Attention (TAA), designed to learn context-dependent alignment for peak demand forecasting. We conduct extensive empirical analysis on two large-scale proprietary datasets from a large e-commerce retailer. We demonstrate that TAT brings up to 30% accuracy improvement on peak demand forecasting while maintaining competitive overall performance compared to other state-of-the-art methods.
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