A critical factor in trustworthy machine learning is to develop robust representations of the training data. Only under this guarantee methods are legitimate to artificially generate data, for example, to counteract imbalanced datasets or provide counterfactual explanations for blackbox decision-making systems. In recent years, Generative Adversarial Networks (GANs) have shown considerable results in forming stable representations and generating realistic data. While many applications focus on generating image data, less effort has been made in generating time series data, especially multivariate signals. In this work, a Transformer-based autoencoder is proposed that is regularized using an adversarial training scheme to generate artificial multivariate time series signals. The representation is evaluated using t-SNE visualizations, Dynamic Time Warping (DTW) and Entropy scores. Our results indicate that the generated signals exhibit higher similarity to an exemplary dataset than using a convolutional network approach.

翻译：可信机器学习中的一个关键因素在于开发训练数据的稳健表示。只有在具备这一保障的前提下，诸如为应对数据集不平衡或为黑箱决策系统提供反事实解释等人工数据生成方法才具有合理性。近年来，生成对抗网络（GANs）在构建稳定表示和生成逼真数据方面取得了显著成果。尽管多数研究聚焦于图像数据生成，但在时间序列数据（尤其是多变量信号）的生成方面仍存在不足。本文提出一种基于Transformer的自编码器，通过对抗训练机制进行正则化，从而生成人工多变量时间序列信号。采用t-SNE可视化、动态时间规整（DTW）及熵值评分对表示质量进行评估。结果表明，相较于卷积网络方法，本方法生成的信号与示例数据集具有更高的相似性。