  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 may struggle with this
flexible and effective utilization. To tackle this challenge, we initially
developed the Time Series Knowledge Base (TSKB) with an advanced indexing
system for efficient historical data retrieval. We also developed the Retrieval
Augmented Cross-Attention (RACA) module, a variant of the cross-attention
mechanism within Transformer's decoder layers, designed to be seamlessly
integrated into the vanilla Transformer architecture to assimilate key
historical data segments. The synergy between TSKB and RACA forms the backbone
of our Retrieval-Augmented Time Series Forecasting (RATSF) framework. Based on
the above two components, RATSF not only significantly enhances performance in
the context of Fliggy hotel service volume forecasting but also adapts flexibly
to various scenarios and integrates with a multitude of Transformer variants
for time-series forecasting. Extensive experimentation has validated the
effectiveness and generalizability of this system design across multiple
diverse contexts.
