arXiv:2606.15172v1 Announce Type: new 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-
The proliferation of AI applications is driving the need for robust generative models, with current solutions often falling short in data-scarce real-world scenarios, prompting innovation in this area.
This development allows AI to create realistic time series data even with limited inputs, which could accelerate development and deployment in fields reliant on complex, hard-to-acquire data.
AI models can now generate more realistic and adaptable synthetic data in situations where real-world data collection is expensive or impractical, broadening the application scope of generative AI.
- · AI researchers
- · Data-scarce industries
- · Machine learning startups
- · Forecasting and simulation sectors
- · Traditional data collection methods
- · Purely data-intensive AI models
Improved synthetic data generation for specialized applications will lead to faster model development and testing.
This could enable new AI-driven insights and automation in previously challenging domains like healthcare, finance, or highly specialized manufacturing.
The reduced reliance on abundant real-world data may democratize access to advanced AI capabilities for smaller entities or niche industries.
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