aiqbench analysis
DAD4TS addresses the critical challenge of limited data in time-series forecasting by combining diffusion models with reinforcement learning to generate high-quality synthetic samples. By projecting data into geometric space rather than using conventional VAE methods, the approach effectively improves forecast accuracy across multiple real-world datasets. This method offers a practical solution for scenarios where training data is scarce, leveraging simultaneous model training to ensure generated samples directly enhance predictive performance.
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 forecast accuracy of the time-series model. To support small-scale data, we use mathematical methods instead of conventional VAE methods to train the diffusion model by projecting the time-series data into the geometric space. We validated the effectiveness of DAD4TS with seven comparative methods through qualitative and quantitative experiments on six real-world datasets and eight time-series models. As a result, DAD4TS was validated on five datasets.
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 forecast accuracy of the time-series model. To support small-scale data, we use mathematical methods instead of conventional VAE methods to train the diffusion model by projecting the time-series data into the geometric space. We validated the effectiveness of DAD4TS with seven comparative methods through qualitative and quantitative experiments on six real-world datasets and eight time-series models. As a result, DAD4TS was validated on five datasets.
Machine Learning (cs.LG)
arXiv:2605.17866 [cs.LG]
(or arXiv:2605.17866v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.17866 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 233 words