Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain
This addresses the problem of limited data for pre-training in time series forecasting, particularly for CloudOps applications, but is incremental as it builds on existing pre-training concepts.
The paper tackled the lack of large-scale datasets for pre-training in time series forecasting by introducing three large datasets from the CloudOps domain, with the largest having billions of observations, and showed that their pre-trained method achieved a 27% reduction in error on the largest dataset.
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.