Long-Term, Multivariate Time Series Generation With the Capture of Intervariate Correlations and Variatewise Characteristics - Kasumi Ohno; Kohei Makino; Makoto Miwa; Yutaka Sasaki (IEEE Access, 2025)
This paper proposes a novel Time Series Generation (TSG) model, the Attended Variate-Conditioned GAN (AVC-GAN), for generating multivariate long-term time series data. Recently, generative approaches to TSG have been explored for applications such as privacy protection, anomaly detection, and time series classification/forecasting. Since existing TSG methods, particularly Generative Adversarial Networks (GANs)-based methods, focus on modeling long-term time series and capturing intervariate correlations, they often struggle to adequately capture the characteristics of each variate, such as specific patterns of waveforms. To address these issues with TSG simultaneously, we propose a novel generative model, AVC-GAN. The key components of AVC-GAN are as follows: 1) a nonautoregressive architecture for modeling long-term time series, 2) variate conditioning for capturing variatewise characteristics, and 3) a multihead attention mechanism for capturing intervariate correlations. Our experiments on multiple benchmark datasets for Long-Term Time Series Forecasting (LTSF) demonstrate that AVC-GAN outperforms state-of-the-art GAN-based generative models across a range of evaluation metrics. In terms of distance-based metrics, calculated as the MSE between real and generated data, our method achieves improvements of 29.8% and 33.7% over state-of-the-art methods in assessing overall intervariate correlations and variatewise characteristics, respectively. Visualization also confirms the superior quality of the generated time series.