DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data
arXiv:2605.17866v2 Announce Type: replace Abstract: Small-scale data is a critical problem in time-series forecasting tasks. Data augmentation is an effective strategy for this task, but it has a limitation in generating meaningful data. To address this limitation, we propose DAD4TS, a diffusion-model-based data augmentation method with reinforcement learning, designed for time-series forecasting with small-scale data. In DAD4TS, a data generator is simultaneously trained with a time-series model and controlled by a reinforcement learning model to efficiently generate samples that improve the
The proliferation of AI applications is increasingly encountering data scarcity, especially for specialized tasks like time-series forecasting, making novel data augmentation techniques highly relevant.
This development addresses a critical bottleneck in AI model training, potentially enabling more robust and accurate forecasting in domains where large datasets are impractical or unavailable.
The ability to generate high-quality synthetic time-series data using diffusion models and reinforcement learning changes how and where AI can be applied, reducing dependency on extensive historical data.
- · AI researchers and developers
- · Industries with limited time-series data (e.g., specific manufacturing, healthca
- · Small and medium-sized enterprises adopting AI
- · Machine learning platforms
- · Companies reliant on large proprietary datasets as a competitive advantage
- · Traditional statistical forecasting methods
Improved accuracy and reliability of time-series forecasting models across various industries due to enhanced data availability.
Accelerated development and adoption of AI solutions in sectors previously constrained by data scarcity.
New business models emerging around synthetic data generation services and specialized AI applications for niche markets.
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