TransFusion: Generating Long, High Fidelity Time Series using Diffusion Models with Transformers
Abstract
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. 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.
Cite
@article{arxiv.2307.12667,
title = {TransFusion: Generating Long, High Fidelity Time Series using Diffusion Models with Transformers},
author = {Md Fahim Sikder and Resmi Ramachandranpillai and Fredrik Heintz},
journal= {arXiv preprint arXiv:2307.12667},
year = {2024}
}