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 …

Transformers have achieved superior performances in many tasks in natural language
processing and computer vision, which also triggered great interest in the time series …

Recent trends in the Machine Learning (ML) and in particular Deep Learning (DL) domains
have demonstrated that with the availability of massive amounts of time series, ML and DL …

Recently, multivariate time series (MTS) forecasting techniques have seen rapid
development and widespread applications across various fields. Transformer-based and …

Long-term time series forecasting has gained significant attention in recent years. While
there are various specialized designs for capturing temporal dependency, previous studies …

Time series forecasting is widely used in extensive applications, such as traffic planning and
weather forecasting. However, real-world time series usually present intricate temporal …

Multivariate time series (MTS) prediction has been widely adopted in various scenarios.
Recently, some methods have employed patching to enhance local semantics and improve …

A time series is a sequence of time-ordered data, and it is generally used to describe how a
phenomenon evolves over time. Time series forecasting, estimating future values of time …

The performance of transformers for time-series forecasting has improved significantly.
Recent architectures learn complex temporal patterns by segmenting a time-series into …