TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting

Huanyu Zhang, Chang Xu, YiFan Zhang, Zhang Zhang, Liang Wang, Tieniu Tan, Jiang Bian

Keywords: time series forecasting, retrieval augmented generation, time series foundation model

Abstract:

Time series forecasting plays a crucial role in data mining, driving rapid advancements across numerous industries.
With the emergence of large models, time series foundation models (TSFMs) have exhibited remarkable generalization capabilities, such as zero-shot learning, through large-scale pre-training.
Meanwhile, Retrieval-Augmented Generation (RAG) methods are widely employed to enhance the performance of foundation models on unseen data, allowing models to access to external knowledge.
In this paper, we introduce **TimeRAF**, a **R**etrieval-**A**ugmented **F**orecasting model that enhance zero-shot time series forecasting through retrieval-augmented techniques.
We develop customized time series knowledge bases that are tailored to the specific forecasting tasks.
TimeRAF employs an end-to-end learnable retriever to extract valuable information from the knowledge base.
Additionally, we propose Channel Prompting for knowledge integration, which effectively extracts relevant information from the retrieved knowledge along the channel dimension.
Extensive experiments demonstrate the effectiveness of our model, showing significant improvement across various domains and datasets.

Primary Area: learning on time series and dynamical systems

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Submission Number: 7472