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

arXiv: https://arxiv.org/abs/2403.04180 · HTML: https://mlsanity.com/paper/2403.04180/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Time series and agricultural RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 25 of its 35 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Placed in

- Retrieval-augmented generation / [Time series and agricultural RAG](https://mlsanity.com/rag/time-series-and-agricultural-rag/)

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021)
- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Generalization through Memorization: Nearest Neighbor Language Models](https://mlsanity.com/paper/1911.00172/) (2019)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting](https://mlsanity.com/paper/2205.14415/) (2022)
- [Long-term Forecasting with TiDE: Time-series Dense Encoder](https://mlsanity.com/paper/2304.08424/) (2023)
- [Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding](https://mlsanity.com/paper/2106.00750/) (2021)
- [ETSformer: Exponential Smoothing Transformers for Time-series Forecasting](https://mlsanity.com/paper/2202.01381/) (2022)
- [Retrieval meets Long Context Large Language Models](https://mlsanity.com/paper/2310.03025/) (2023)
- [Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2206.13816/) (2022)
- [Controllable Semantic Parsing via Retrieval Augmentation](https://mlsanity.com/paper/2110.08458/) (2021)
- [Retrieval Based Time Series Forecasting](https://mlsanity.com/paper/2209.13525/) (2022)
- [From Known to Unknown: Knowledge-guided Transformer for Time-Series Sales Forecasting in Alibaba](https://mlsanity.com/paper/2109.08381/) (2021)
- [MQRetNN: Multi-Horizon Time Series Forecasting with Retrieval Augmentation](https://mlsanity.com/paper/2207.10517/) (2022)
- [DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition](https://mlsanity.com/paper/2305.03688/) (2023)
- [Retrieval-Augmented Transformer-XL for Close-Domain Dialog Generation](https://mlsanity.com/paper/2105.09235/) (2021)

## Built on by (most cited first, 3 of 3)

- [Retrieval Augmented Time Series Forecasting](https://mlsanity.com/paper/2411.08249/) (2024)
- [TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting](https://mlsanity.com/paper/2412.20810/) (2024)
- [Memory in Deep Time-Series Models](https://mlsanity.com/paper/2609.06006/) (2026)

## Abstract (arXiv)

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.