One-Pass Generation of Multivariate Time Series through Conditional Multivariate Modeling

In recent years, exploring deep generative models for generating time series has garnered significant interest within the research community. These models have found wide-ranging applications in areas such as data augmentation, scenario simulation, and the imputation of missing data. The authenticity of the generated time series has seen remarkable advancements with the integration of recurrent neural networks (RNNs) and generative adversarial networks (GANs). RNNs used to represent the state-of-the-art (SOA) in processing sequence dependencies until the advent of Transformers, which redefined the SOA, especially in Natural Language Processing and Computer Vision. The introduction of a transformer-based GAN represented an innovative step forward, aiming to address the limitations inherent in RNNs. However, this model’s efficacy is constrained when faced with unimodal data distribution assumptions, leading to arbitrary outputs in complex distribution scenarios. This paper introduces a novel Multivariate Time Series Conditional GAN (MTS-CGAN), that leverages transformer-based architectures in generator and discriminator networks. MTS-CGAN conditions the generation process on a specific encoded context (categorical and MTS inputs), enabling one-pass generation of multivariate time series, and accommodating mixed distribution frameworks, outperforming existing models. We evaluate MTS-CGAN using quantitative metrics across multiple multivariate time series datasets. Furthermore, we propose also an innovative adaptation of the Frechet Inception Distance (FID), tailored for time series, to assess the quality of the generated data. This research demonstrates the potential of MTS-CGAN in generating high-fidelity multivariate time series.