The industrial process consumes substantial energy and emits large amounts of carbon
dioxide. With the help of accurate energy consumption and carbon emissions forecasting …

Neural networks have been successfully employed in various domains such as
classification, regression and clustering, etc. Generally, the back propagation (BP) based …

Estimating the price of crude oil, which is seen as an important resource for economic
development and stability in the world, is a topic of great interest by policy makers and …

Accurate train arrival delay prediction is critical for real-time train dispatching and for the
improvement of the transportation service. This study proposes a data-driven method that …

Achieving the desired accuracy in time series forecasting has become a binding domain,
and develo** a forecasting framework with a high degree of accuracy is one of the most …

Crude oil price prediction helps to get a better understanding of the global economic
situation. Recently, variational mode decomposition (VMD) is introduced into the field of …

This paper proposes a three-stage online deep learning model for time series based on the
ensemble deep random vector functional link (edRVFL). The edRVFL stacks multiple …

Oil price forecasting has received a prodigious attention by scholars and policymakers due
to its significant effect on various economic sectors and markets. Incentivized by this issue …

The prediction of crude oil prices has important research significance. The paper contributes
to the literature of hybrid models for forecasting crude oil prices. We apply ensemble …

Highlights•A hybrid forecasting system is developed for crude oil price forecasting.•The
improved multi-objective water cycle algorithm is proposed.•The proposed prediction system …