Enhancing Long Time-Series Data Augmentation with Generative Adversarial Networks
Adv. Sci. Technol. Eng. Syst. J. 3(3), 40–49 (2026);
DOI: 10.25046/aj110303
With the development of deep learning, time-series-related tasks have been increasingly applied across various fields. However, time-series data used in the medical and semiconductor industries are often different from those in daily life, with high sampling frequencies and very long sequence lengths, and collecting such data is usually very challenging. Therefore, data augmentation is a significant part of applying such long time-series data to deep learning tasks. In this study, the autoregressive model used in the TimeGAN method is replaced with IndRNN to generate long time-series data. The experimental results also show that, as the sequence length increases, this simple substitution can achieve a strong data augmentation effect and gradually extend to longer time-series data. Furthermore, the practical use of the generated time-series data in stock prediction tasks demonstrates the effectiveness of data augmentation, particularly for longer time-series data. This practical application provides a more direct illustration of the capability to perform data augmentation for long time-series data.
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