RATSF: Empowering Customer Service Volume Management through
  Retrieval-Augmented Time-Series Forecasting
Tianfeng Wang Gaojie Cui
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.