Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …

Time-Series Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …

Time series analysis is widely used in extensive areas. Recently, to reduce labeling
expenses and benefit various tasks, self-supervised pre-training has attracted immense …

The past decade has witnessed significant advances in time series modeling with deep
learning. While achieving state-of-the-art results, the best-performing architectures vary …

Time series, characterized by a sequence of data points arranged in a discrete-time order,
are ubiquitous in real-world applications. Different from other modalities, time series present …

Time series forecasting is crucial for several fields, such as disaster warning, weather
prediction, and energy consumption. Transformer-based models are considered to have …

Many unsupervised methods have recently been proposed for multivariate time series
anomaly detection. However, existing works mainly focus on stable data yet often omit the …

Contrastive learning methods have shown an impressive ability to learn meaningful
representations for image or time series classification. However, these methods are less …

Self-supervised learning has garnered significant attention for its ability to learn meaningful
representations. Recent advancements have introduced self-supervised methods for time …

Deep learning has contributed remarkably to the advancement of time series analysis. Still,
deep models can encounter performance bottlenecks in real-world small-sample scenarios …