The generation of high-quality, long-sequenced time-series data is essential due to its wide range of applications. In the past, standalone Recurrent and Convolutional Neural Network-based Generative Adversarial Networks (GAN) were used to synthesize time-series data. However, they are inadequate for generating long sequences of time-series data due to limitations in the architecture. Furthermore, GANs are well known for their training instability and mode collapse problem. To address this, we propose TransFusion, a diffusion, and transformers-based generative model to generate high-quality long-sequence time-series data. We have stretched the sequence length to 384, and generated high-quality synthetic data. To the best of our knowledge, this is the first study that has been done with this long-sequence length. Also, we introduce two evaluation metrics to evaluate the quality of the synthetic data as well as its predictive characteristics. We evaluate TransFusion with a wide variety of visual and empirical metrics, and TransFusion outperforms the previous state-of-the-art by a significant margin.

我们通过引入TransFusion，一种基于扩散和变换器的生成模型，生成了高质量、长序列的时间序列数据，并引入了两种评估度量来评估合成数据的质量和预测特性。TransFusion在各种视觉和经验度量方面优于先前的最先进方法。

TransFusion: 使用Transformer和扩散模型生成长且高保真度的时间序列