RATSF: Empowering Customer Service Volume Management through Retrieval-Augmented Time-Series Forecasting

arXiv:2403.04180v1 Announce Type: new
Abstract: 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 …

abstract arxiv cs.ir cs.lg customer customer service data forecasting historical data management patterns retrieval retrieval-augmented rnn series service service management summarizing through transformer type