Generative models for images have gained significant attention in computer vision and natural language processing due to their ability to generate realistic samples from complex data distributions. To leverage the advances of image-based generative models for the time series domain, we propose a two-dimensional image representation for time series, the Extended Intertemporal Return Plot (XIRP). Our approach captures the intertemporal time series dynamics in a scale-invariant and invertible way, reducing training time and improving sample quality. We benchmark synthetic XIRPs obtained by an off-the-shelf Wasserstein GAN with gradient penalty (WGAN-GP) to other image representations and models regarding similarity and predictive ability metrics. Our novel, validated image representation for time series consistently and significantly outperforms a state-of-the-art RNN-based generative model regarding predictive ability. Further, we introduce an improved stochastic inversion to substantially improve simulation quality regardless of the representation and provide the prospect of transfer potentials in other domains.

翻译：图像生成模型因其能够从复杂数据分布中生成逼真样本的能力，在计算机视觉和自然语言处理领域备受关注。为将基于图像的生成模型的进展应用于时间序列领域，我们提出了一种二维图像表示方法——扩展跨期返回图（XIRP）。该方法以尺度不变且可逆的方式捕捉时间序列的跨期动态特性，从而缩短训练时间并提升样本质量。我们将通过现成的带梯度惩罚的Wasserstein生成对抗网络（WGAN-GP）获得的合成XIRP，与其他图像表示及模型在相似性和预测能力指标上进行了基准测试。这种新颖且经过验证的时间序列图像表示方法在预测能力上持续且显著地优于当前最先进的基于RNN的生成模型。此外，我们引入了一种改进的随机逆变换方法，可大幅提升模拟质量（无论采用何种表示方式），并展示了在其他领域迁移应用的前景。