TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster

• URL: http://arxiv.org/abs/2503.07649v2
• Date: Tue, 01 Apr 2025 21:23:59 GMT
• Title: TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster
• Authors: Kanghui Ning, Zijie Pan, Yu Liu, Yushan Jiang, James Y. Zhang, Kashif Rasul, Anderson Schneider, Lintao Ma, Yuriy Nevmyvaka, Dongjin Song,
• Abstract summary: Time series foundation models (TSFMs) lack inherent mechanisms for domain adaptation and suffer from limited interpretability.<n>We present TS-RAG, a retrieval-augmented generation based time series forecasting framework.<n>We show that TS-RAG achieves state-of-the-art zero-shot forecasting performance, outperforming TSFMs by up to 6.51% across diverse domains.
• Score: 14.512119661418522
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, Large Language Models (LLMs) and Foundation Models (FMs) have become prevalent for time series forecasting tasks. However, fine-tuning large language models (LLMs) for forecasting enables the adaptation to specific domains but may not generalize well across diverse, unseen datasets. Meanwhile, existing time series foundation models (TSFMs) lack inherent mechanisms for domain adaptation and suffer from limited interpretability, making them suboptimal for zero-shot forecasting. To this end, we present TS-RAG, a retrieval-augmented generation based time series forecasting framework that enhances the generalization capability and interpretability of TSFMs. Specifically, TS-RAG leverages pre-trained time series encoders to retrieve semantically relevant time series segments from a dedicated knowledge database, incorporating contextual patterns for the given time series query. Next, we develop a learnable Mixture-of-Experts (MoE)-based augmentation module, which dynamically fuses retrieved time series patterns with the TSFM's representation of the input query, improving forecasting accuracy without requiring task-specific fine-tuning. Thorough empirical studies on seven public benchmark datasets demonstrate that TS-RAG achieves state-of-the-art zero-shot forecasting performance, outperforming TSFMs by up to 6.51% across diverse domains and showcasing desired interpretability.

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