Computer Science > Machine Learning
[Submitted on 7 Mar 2024 (v1), last revised 16 Jun 2024 (this version, v2)]
Title:RATSF: Empowering Customer Service Volume Management through Retrieval-Augmented Time-Series Forecasting
View PDF HTML (experimental)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 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.
Submission history
From: Tianfeng Wang [view email][v1] Thu, 7 Mar 2024 03:23:13 UTC (667 KB)
[v2] Sun, 16 Jun 2024 15:59:13 UTC (717 KB)
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