Generating time series data is a promising approach to address data deficiency problems. However, it is also challenging due to the complex temporal properties of time series data, including local correlations as well as global dependencies. Most existing generative models have failed to effectively learn both the local and global properties of time series data. To address this open problem, we propose a novel time series generative model named 'Time-Transformer AAE', which consists of an adversarial autoencoder (AAE) and a newly designed architecture named 'Time-Transformer' within the decoder. The Time-Transformer first simultaneously learns local and global features in a layer-wise parallel design, combining the abilities of Temporal Convolutional Networks and Transformer in extracting local features and global dependencies respectively. Second, a bidirectional cross attention is proposed to provide complementary guidance across the two branches and achieve proper fusion between local and global features. Experimental results demonstrate that our model can outperform existing state-of-the-art models in 5 out of 6 datasets, specifically on those with data containing both global and local properties. Furthermore, we highlight our model's advantage on handling this kind of data via an artificial dataset. Finally, we show our model's ability to address a real-world problem: data augmentation to support learning with small datasets and imbalanced datasets.

翻译：生成时间序列数据是解决数据匮乏问题的一种有前景的方法。然而，由于时间序列数据兼具局部相关性和全局依赖性的复杂时间特性，这一任务同样面临挑战。现有生成模型大多未能有效学习时间序列数据的局部与全局属性。为应对这一开放性问题，我们提出了名为"Time-Transformer AAE"的新型时间序列生成模型，该模型由对抗自编码器（AAE）与解码器中新设计的"Time-Transformer"架构组成。Time-Transformer首先通过层级并行设计同时学习局部与全局特征，分别结合时序卷积网络在局部特征提取和Transformer在全局依赖性捕获方面的能力；其次提出双向交叉注意力机制，为两个分支提供互补引导，实现局部与全局特征的恰当融合。实验结果表明，在6个数据集的5个中，我们的模型能超越现有最先进模型，特别是在包含全局与局部属性的数据上。此外，我们通过人工数据集凸显了模型在处理此类数据上的优势。最后，我们展示了模型在解决实际问题——通过数据增强支持小样本与不平衡数据集学习——的能力。