Towards a Unified Generative Model for Scarce Time Series with Domain Experts

• URL: http://arxiv.org/abs/2606.15172v1
• Date: Sat, 13 Jun 2026 07:41:42 GMT
• Title: Towards a Unified Generative Model for Scarce Time Series with Domain Experts
• Abstract summary: TimeMoDE is a novel framework that integrates Diffusion Transformers with Mixture-of-Experts to exploit time series generation under data scarcity.<n>It is pre-trained on a large-scale collection of multi-domain datasets to extract domain-agnostic temporal representations.<n>Extensive experiments demonstrate that TimeMoDE outperforms existing methods under diverse low-data settings.
• Score: 14.03756371866814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthesizing realistic time series with generative models has wide-ranging applications in real-world scenarios. Despite recent progress, most existing methods are trained under the assumption of abundant training data, which substantially limits their effectiveness in data-scarce settings. In this paper, we propose TimeMoDE, a novel framework that integrates Diffusion Transformers with Mixture-of-Experts to exploit both domain adaptability and diffusion-stage awareness for time series generation under data scarcity. It is pre-trained on a large-scale collection of multi-domain datasets to extract domain-agnostic temporal representations and domain-specific information benefiting generalization during fine-tuning. We propose Domain Prompts to condition expert assignment for indistinguishable noised tokens, mitigating the limitations of capturing inter-dataset relationships. Moreover, we incorporate diffusion timestep signals to equip the experts with awareness of time series degradation variations, facilitating adaptive calibrate to stage-dependent denoising requirements. Extensive experiments demonstrate that TimeMoDE outperforms existing methods under diverse low-data settings. It establishes an innovative paradigm for advanced time series few-shot generation.

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