  This paper shows that masked autoencoder with extrapolator (ExtraMAE) is a
scalable self-supervised model for time series generation. ExtraMAE randomly
masks some patches of the original time series and learns temporal dynamics by
recovering the masked patches. Our approach has two core designs. First,
ExtraMAE is self-supervised. Supervision allows ExtraMAE to effectively and
efficiently capture the temporal dynamics of the original time series. Second,
ExtraMAE proposes an extrapolator to disentangle two jobs of the decoder:
recovering latent representations and mapping them back into the feature space.
These unique designs enable ExtraMAE to consistently and significantly
outperform state-of-the-art (SoTA) benchmarks in time series generation. The
lightweight architecture also makes ExtraMAE fast and scalable. ExtraMAE shows
outstanding behavior in various downstream tasks such as time series
classification, prediction, and imputation. As a self-supervised generative
model, ExtraMAE allows explicit management of the synthetic data. We hope this
paper will usher in a new era of time series generation with self-supervised
models.
