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

TL;DR

DAD4TS introduces a diffusion-model-based data augmentation approach with reinforcement learning to enhance time-series forecasting accuracy on small datasets.

Contribution

It presents a novel diffusion model trained with mathematical methods and reinforcement learning for effective data augmentation in small-scale time-series forecasting.

Findings

DAD4TS outperforms seven comparative methods in experiments.

Validated on five real-world datasets with six different models.

Improves forecast accuracy significantly on small-scale data.

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.