Time Series Generation with Adversarial Autoencoders and Autoregressive Refinement
AVATAR is a generative framework for synthetic time-series data that integrates an adversarial autoencoder with autoregressive learning trained jointly under a shared supervision signal. A distribution-matching loss aligns the aggregated posterior of the latent space with the prior, while an innovation-driven autoregressive refinement step corrects exposure bias so that generated sequences preserve both the stepwise temporal dynamics and the joint distribution of the training data. Evaluated on benchmark multivariate datasets, it improves fidelity (discriminative score) and utility (predictive score) over prior GAN-based time-series generators such as TimeGAN.
AVATAR: Adversarial Autoencoders with Autoregressive Refinement for Time Series Generation
AVATAR is a framework for generating synthetic multivariate time series that combines an adversarial autoencoder (AAE) with autoregressive learning. An AAE is an autoencoder whose latent code is push…