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 …

Deep learning models and graphics processing units have completely transformed the field
of machine learning. Recurrent neural networks and long short-term memories have been …

Many real-world applications require the prediction of long sequence time-series, such as
electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a …

With the development of communication, the Internet of Things (IoT) has been widely
deployed and used in industrial manufacturing, intelligent transportation, and healthcare …

Long-term prediction of multivariate time series is still an important but challenging problem.
The key to solve this problem is capturing (1) the spatial correlations at the same time,(2) the …

Multivariate time series (MTS) forecasting is widely used in various domains, such as
meteorology and traffic. Due to limitations on data collection, transmission, and storage, real …

Deep learning models have the potential to advance the short-term decision-making of
electricity market participants and system operators by capturing the complex dependences …

Forecasting of multivariate time-series is an important problem that has applications in traffic
management, cellular network configuration, and quantitative finance. A special case of the …

Computing trajectory similarities is a critical and fundamental task for various spatial-
temporal applications, such as clustering, prediction, and anomaly detection. Traditional …

Heat load prediction is essential for energy efficiency and carbon reduction in district heating
systems. However, heat load is influenced by many factors, such as building characteristics …