Title: Time-Transformer AAE: Connecting Temporal Convolutional Networks and Transformer for Time Series Generation. Abstract: Generating time series data is a challenging task due to the complex temporal properties of this type of data. Such temporal properties typically include 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 consisting of an adversarial autoencoder (AAE) and a newly designed architecture named `Time-Transformer' within the decoder. We call this generative model `Time-Transformer AAE'. The Time-Transformer first simultaneously learns local and global features in a layer-wise parallel design, combining the abilities of Temporal Convolutional Networks (TCN) 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 most cases, especially when the data contains both global and local properties. We also show our model's ability to perform a downstream task: data augmentation to support the solution of imbalanced classification problems.