Effective wind speed forecasting has great significance for urban energy system operations
and the construction of low-carbon cities. However, most previous research has focused …

Wind speed forecasting is of prime importance for wind power generation, which can bring
tremendous economic, social and environmental benefits. However, previous wind speed …

Forecasting complex time series faces a huge challenge due to its high volatility. To improve
the accuracy and robustness of prediction, this paper proposes a bi‐level ensemble learning …

Carbon trading is one of the strategies to achieve the goal of dual carbon. However, carbon
prices exhibit non-stationary and nonlinear characteristics, posing significant challenges for …

Abstract Coronavirus disease 2019 (COVID-19) is a pandemic that has affected all countries
in the world. The aim of this study is to examine the potential advantages of Singular …

In deep learning, it is common to encounter data observed at different frequencies. Mixed
data sampling (MIDAS) is an efficient technique for handling mixed frequency data, where a …

Gross domestic product (GDP) is an important indicator for determining a country's or
region's economic status and development level, and it is closely linked to inflation …

Improving wind speed prediction is essential for increasing the use of wind energy and
promoting sustainable utilization of resources. Most previous studies relied on single-valued …

Singular spectrum analysis (SSA) is a non-parametric forecasting and filtering method that
has many applications in a variety of fields such as signal processing, economics and time …

In this paper, the role of the El Niño‐Southern Oscillation (ENSO), measured by the
Equatorial Southern Oscillation Index (EQSOI), is used to formally forecast the inflation and …