  Time-series analysis plays a pivotal role across a range of critical
applications, from finance to healthcare, which involves various tasks, such as
forecasting and classification. To handle the inherent complexities of
time-series data, such as high dimensionality and noise, traditional supervised
learning methods first annotate extensive labels for time-series data in each
task, which is very costly and impractical in real-world applications. In
contrast, pre-trained foundation models offer a promising alternative by
leveraging unlabeled data to capture general time series patterns, which can
then be fine-tuned for specific tasks. However, existing approaches to
pre-training such models typically suffer from high-bias and low-generality
issues due to the use of predefined and rigid augmentation operations and
domain-specific data training. To overcome these limitations, this paper
introduces UniCL, a universal and scalable contrastive learning framework
designed for pretraining time-series foundation models across cross-domain
datasets. Specifically, we propose a unified and trainable time-series
augmentation operation to generate pattern-preserved, diverse, and low-bias
time-series data by leveraging spectral information. Besides, we introduce a
scalable augmentation algorithm capable of handling datasets with varying
lengths, facilitating cross-domain pretraining. Extensive experiments on two
benchmark datasets across eleven domains validate the effectiveness of UniCL,
demonstrating its high generalization on time-series analysis across various
fields.
