  Time series has been left behind in the era of pre-training and transfer
learning. While research in the fields of natural language processing and
computer vision are enjoying progressively larger datasets to train massive
models, the most popular time series datasets consist of only tens of thousands
of time steps, limiting our ability to study the effectiveness of pre-training
and scaling. Recent studies have also cast doubt on the need for expressive
models and scale. To alleviate these issues, we introduce three large-scale
time series forecasting datasets from the cloud operations (CloudOps) domain,
the largest having billions of observations, enabling further study into
pre-training and scaling of time series models. We build the empirical
groundwork for studying pre-training and scaling of time series models and pave
the way for future research by identifying a promising candidate architecture.
We show that it is a strong zero-shot baseline and benefits from further
scaling, both in model and dataset size. Accompanying these datasets and
results is a suite of comprehensive benchmark results comparing classical and
deep learning baselines to our pre-trained method - achieving a 27% reduction
in error on the largest dataset. Code and datasets can be found
https://github.com/SalesforceAIResearch/pretrain-time-series-cloudops.
