Summary of Ratsf: Empowering Customer Service Volume Management Through Retrieval-augmented Time-series Forecasting, by Tianfeng Wang et al.
RATSF: Empowering Customer Service Volume Management through Retrieval-Augmented Time-Series Forecasting
by Tianfeng Wang, Gaojie Cui
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Retrieval-Augmented Time Series Forecasting (RATSF) framework is designed to improve customer service management by accurately predicting service volume. The authors address the challenge of non-stationarity in time series data by developing a Time Series Knowledge Base (TSKB) with an advanced indexing system and a Retrieval Augmented Cross-Attention (RACA) module, which integrates seamlessly into the Transformer architecture. The RACA module assimilates key historical data segments, enhancing performance in forecasting Fliggy hotel service volume and adapting to various scenarios. Extensive experimentation validates the effectiveness and generalizability of this framework across diverse contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict customer demand is proposed. Right now, predicting how many people will need help with things like booking hotels or answering questions takes a lot of data from the past to make good predictions. But, sometimes that old data isn’t very helpful because things have changed since then. To solve this problem, the authors created a special system that can quickly find and use the most important pieces of old data. This helps make better predictions about what will happen in the future. The new system is called Retrieval-Augmented Time Series Forecasting (RATSF), and it does well even when trying to predict different things or working with different types of data. |
Keywords
* Artificial intelligence * Cross attention * Knowledge base * Time series * Transformer
