Time-series forecasting is a significant discipline of data modeling where past observations
of the same variable are analyzed to predict the future values of the time series. Its …

Time series forecasting is fundamental for various use cases in different domains such as
energy systems and economics. Creating a forecasting model for a specific use case …

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 …

Here we represent human lives in a way that shares structural similarity to language, and we
exploit this similarity to adapt natural language processing techniques to examine the …

In recent years, machine-learning techniques, particularly deep learning, have outperformed
traditional time-series forecasting approaches in many contexts, including univariate and …

Predicting electricity demand data is considered an essential task in decisions taking, and
establishing new infrastructure in the power generation network. To deliver a high-quality …

A growing interest in renewable power increases its impact on the energy grid, posing
significant challenges to reliability, stability, and planning. Weather-based prediction …

Perceiving the future trend of Vessel Traffic Flow (VTF) in advance has great application
values in the maritime industry. However, using such big data from the Automatic …

RNN-based methods have faced challenges in the Long-term Time Series Forecasting
(LTSF) domain when dealing with excessively long look-back windows and forecast …

The data produced by millions of connected devices and smart sensors in the Industrial
Internet of Things (IIoT) is highly dynamic, large-scale, heterogeneous, and time-stamped …