An efficient customer service management system hinges on precise forecasting of service volume. In this scenario, where data non-stationarity is pronounced, successful forecasting heavily relies on identifying and leveraging similar historical data rather than merely summarizing periodic patterns. Existing models based on RNN or Transformer architectures often struggle with this flexible and effective utilization. To address this challenge, we propose an efficient and adaptable cross-attention module termed RACA, which effectively leverages historical segments in forecasting task, and we devised a precise representation scheme for querying historical sequences, coupled with the design of a knowledge repository. These critical components collectively form our Retrieval-Augmented Temporal Sequence Forecasting framework (RATSF). RATSF not only significantly enhances performance in the context of Fliggy hotel service volume forecasting but, more crucially, can be seamlessly integrated into other Transformer-based time-series forecasting models across various application scenarios. Extensive experimentation has validated the effectiveness and generalizability of this system design across multiple diverse contexts.

翻译：高效的客户服务管理体系依赖于服务量的精准预测。在数据非平稳性显著的场景下，成功预测的关键在于识别并利用相似历史数据，而非仅归纳周期性模式。基于RNN或Transformer架构的现有模型往往难以实现这种灵活且有效的利用。为解决这一挑战，我们提出了一种高效且可适配的交叉注意力模块RACA，该模块在预测任务中有效利用历史片段，同时设计了用于查询历史序列的精确表示方案，并构建了知识库。这些关键组件共同构成了我们的检索增强时间序列预测框架（RATSF）。RATSF不仅在飞猪酒店服务量预测场景中显著提升了性能，更重要的是，可无缝集成至各类应用场景下其他基于Transformer的时间序列预测模型中。大量实验验证了该系统设计在多种不同场景下的有效性与泛化能力。