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 …

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 …

Forecasting complex time series is ubiquitous and vital in a range of applications but
challenging. Recent advances endeavor to achieve progress by incorporating various deep …

Deep learning models have progressively advanced time series forecasting due to their
powerful capacity in capturing sequence dependence. Nevertheless, it is still challenging to …

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 …

The monitoring and management of numerous and diverse time series data at Alibaba
Group calls for an effective and scalable time series anomaly detection service. In this paper …

Data Augmentation (DA) has become a critical approach in Time Series Classification
(TSC), primarily for its capacity to expand training datasets, enhance model robustness …

The decomposition of time series into components is an important task that helps to
understand time series and can enable better forecasting. Nowadays, with high sampling …

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 …