One-Embedding-Fits-All: Efficient Zero-Shot Time Series Forecasting by a Model Zoo

• URL: http://arxiv.org/abs/2509.04208v2
• Date: Thu, 25 Sep 2025 13:20:23 GMT
• Title: One-Embedding-Fits-All: Efficient Zero-Shot Time Series Forecasting by a Model Zoo
• Authors: Hao-Nan Shi, Ting-Ji Huang, Lu Han, De-Chuan Zhan, Han-Jia Ye,
• Abstract summary: Time Series Foundation Models (TSFMs) have significantly advanced zero-shot forecasting.<n>No single TSFM excels universally, as different models exhibit preferences for distinct temporal patterns.<n>We propose ZooCast, which characterizes each model's distinct forecasting strengths.
• Score: 82.65837388129746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proliferation of Time Series Foundation Models (TSFMs) has significantly advanced zero-shot forecasting, enabling predictions for unseen time series without task-specific fine-tuning. Extensive research has confirmed that no single TSFM excels universally, as different models exhibit preferences for distinct temporal patterns. This diversity suggests an opportunity: how to take advantage of the complementary abilities of TSFMs. To this end, we propose ZooCast, which characterizes each model's distinct forecasting strengths. ZooCast can intelligently assemble current TSFMs into a model zoo that dynamically selects optimal models for different forecasting tasks. Our key innovation lies in the One-Embedding-Fits-All paradigm that constructs a unified representation space where each model in the zoo is represented by a single embedding, enabling efficient similarity matching for all tasks. Experiments demonstrate ZooCast's strong performance on the GIFT-Eval zero-shot forecasting benchmark while maintaining the efficiency of a single TSFM. In real-world scenarios with sequential model releases, the framework seamlessly adds new models for progressive accuracy gains with negligible overhead.

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