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, a crucial technique for achieving artificial intelligence (AI), has been
successfully applied in many fields. The gradual application of the latest architectures of …

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 plays an important role in the automation and
optimization of intelligent applications. It is a challenging task, as we need to consider both …

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 …

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

Many real-world applications show growing demand for the prediction of long sequence
time-series, such as electricity consumption planning. Long sequence time-series …

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 …