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 …

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

Long-term time series forecasting is challenging since prediction accuracy tends to
decrease dramatically with the increasing horizon. Although Transformer-based methods …

Recent studies have shown that deep learning models such as RNNs and Transformers
have brought significant performance gains for long-term forecasting of time series because …

Deep learning performs remarkably well on many time series analysis tasks recently. The
superior performance of deep neural networks relies heavily on a large number of training …

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

Time series forecasting is a critical and challenging problem in many real applications.
Recently, Transformer-based models prevail in time series forecasting due to their …

Recently, frequency transformation (FT) has been increasingly incorporated into deep
learning models to significantly enhance state-of-the-art accuracy and efficiency in time …

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 …

The study of natural and human-made processes often results in long sequences of
temporally-ordered values, aka time series (TS). Such processes often consist of multiple …