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 …

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 …

In recent times, deep artificial neural networks have achieved many successes in pattern
recognition. Part of this success can be attributed to the reliance on big data to increase …

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 …

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

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 …

The nature of streamflow in the basins is stochastic and complex making it difficult to make
an accurate prediction about the future river flows. Recently, artificial neural-based deep …