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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.
Wang et al. (Wed,) studied this question.