The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …

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 …

Recently many deep models have been proposed for multivariate time series (MTS)
forecasting. In particular, Transformer-based models have shown great potential because …

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

Transformer architectures have widespread applications, particularly in Natural Language
Processing and Computer Vision. Recently, Transformers have been employed in various …

Time series anomaly detection is a challenging problem due to the complex temporal
dependencies and the limited label data. Although some algorithms including both …

Multivariate time series forecasting plays a pivotal role in contemporary web technologies. In
contrast to conventional methods that involve creating dedicated models for specific time …

Spatio-temporal graph neural networks (STGNN) have emerged as the dominant model for
spatio-temporal graph (STG) forecasting. Despite their success, they fail to model intrinsic …

Accurate prediction of electric load is crucial in power grid planning and management. In this
paper, we solve the electric load forecasting problem under extreme events such as …

Time series analysis is ubiquitous and important in various areas, such as Artificial
Intelligence for IT Operations (AIOps) in cloud computing, AI-powered Business Intelligence …