DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data

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, designed for time-series forecasting with small-scale data. In DAD4TS, to generate samples that improve the predictive accuracy of a time-series forecasting model, the generator and the time-series forecasting model are trained simultaneously, and the data generator is updated while calculating the usefulness of the generated data. Furthermore, 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 latent space. We validated the effectiveness of DAD4TS with eight comparative methods through qualitative and quantitative experiments on six real-world datasets and six time-series models. As a result, DAD4TS showed improvements across multiple datasets.