Time Series Generation with Masked Autoencoder

This paper shows that masked autoencoders with interpolators (InterpoMAE) are scalable self-supervised generators for time series. InterpoMAE masks random patches from the input time series and restore the missing patches in the latent space by an interpolator. The core design is that InterpoMAE uses an interpolator rather than mask tokens to restore the latent representations for missing patches in the latent space. This design enables more efficient and effective capture of temporal dynamics with bidirectional information. InterpoMAE allows for explicit control on the diversity of synthetic data by changing the size and number of masked patches. Our approach consistently and significantly outperforms state-of-the-art (SoTA) benchmarks of unsupervised learning in time series generation on several real datasets. Synthetic data produced show promising scaling behavior in various downstream tasks such as data augmentation, imputation and denoise.