TransFusion: Generating Long, High Fidelity Time Series using Diffusion Models with Transformers

• URL: http://arxiv.org/abs/2307.12667v2
• Date: Wed, 24 Apr 2024 16:38:32 GMT
• Title: TransFusion: Generating Long, High Fidelity Time Series using Diffusion Models with Transformers
• Authors: Md Fahim Sikder, Resmi Ramachandranpillai, Fredrik Heintz,
• Abstract summary: 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.
• Score: 3.2623791881739033
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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