TransFusion: Generating Long, High Fidelity Time Series using Diffusion
  Models with Transformers
Md Fahim Sikder Resmi Ramachandranpillai Fredrik Heintz
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.