A Contemporary Survey on Multisource Information Fusion for Smart Sustainable Cities: Emerging Trends and Persistent Challenges
The emergence of smart sustainable cities has unveiled a wealth of data sources, each
contributing to a vast array of urban applications. At the heart of managing this plethora of …
contributing to a vast array of urban applications. At the heart of managing this plethora of …
[HTML][HTML] Evaluating Wind Speed Forecasting Models: A Comparative Study of CNN, DAN2, Random Forest and XGBOOST in Diverse South African Weather …
The main source of electricity worldwide stems from fossil fuels, contributing to air pollution,
global warming, and associated adverse effects. This study explores wind energy as a …
global warming, and associated adverse effects. This study explores wind energy as a …
A three-stage prediction model for firm default risk: An integration of text sentiment analysis
X Ma, T Che, Q Jiang - Omega, 2025 - Elsevier
Predicting firm default risk is vital for financial institutions to avert significant economic
losses, making the enhancement of its prediction precision both imperative and intricate …
losses, making the enhancement of its prediction precision both imperative and intricate …
[HTML][HTML] A new feature selection method based on importance measures for crude oil return forecasting
This paper introduces a novel feature selection method, called Feature Selection based on
Importance Measures (FS-IM), to enhance the forecasting of crude oil returns. FS-IM …
Importance Measures (FS-IM), to enhance the forecasting of crude oil returns. FS-IM …
An ocean tidal energy point-interval forecasting system based on enhanced auxiliary feature, mode decomposition combined with compressive sensing and attention …
H Yang, Q Wu, G Li - Journal of Cleaner Production, 2024 - Elsevier
Promoting the transformation of energy structure and developing clean energy technology
has become a common consensus. As a clean energy, the accurate forecasting of tidal …
has become a common consensus. As a clean energy, the accurate forecasting of tidal …
[HTML][HTML] Time-mixing and feature-mixing modelling for realized volatility forecast: Evidence from TSMixer model
HG Souto, SK Heuvel, FL Neto - The Journal of Finance and Data Science, 2024 - Elsevier
This study evaluates the effectiveness of the TSMixer neural network model in forecasting
stock realized volatility, comparing it with traditional and contemporary benchmark models …
stock realized volatility, comparing it with traditional and contemporary benchmark models …
The Research on Deep Learning-Driven Dimensionality Reduction and Strain Prediction Techniques Based on Flight Parameter Data
W Huang, R Wang, M Zhang, Z Yin - Applied Sciences, 2024 - mdpi.com
Loads and strains in critical areas play a crucial role in aircraft structural health monitoring,
the tracking of individual aircraft lifespans, and the compilation of load spectra. Direct …
the tracking of individual aircraft lifespans, and the compilation of load spectra. Direct …
Forecasting the high‐frequency volatility based on the LSTM‐HIT model
G Liu, Z Zhuang, M Wang - Journal of Forecasting, 2024 - Wiley Online Library
Volatility forecasting from high‐frequency data plays a crucial role in many financial fields,
such as risk management, option pricing, and portfolio management. Many existing …
such as risk management, option pricing, and portfolio management. Many existing …