The application of machine learning techniques in the field of tourism is experiencing a
remarkable growth, as they allow to propose efficient solutions to problems present in this …

Rising market demands, economic pressures, and technological advancements have
spurred researchers to seek ways to enhance business environments and scientific …

Cross-modal hash has become a key technology for large datasets retrieval. However, some
challenges still need to be tackled: 1) How to effectively embed semantic information into …

This study delves into the intricate process of predicting tourism demand, explicitly focusing
on econometric and quantitative time series analysis. A meticulous review of the existing …

Accurate time series forecasting is crucial in various fields, including finance, economics,
healthcare, transportation, and energy. Recently, deep learning methods have gained …

For the purpose of managing financial risk and making investment decisions, interval stock
price forecasting is essential. Currently, decomposition integration frameworks are widely …

The integration of renewable energy sources in power systems has resulted in increased
complexity in dispatch management, necessitating higher accuracy in short-term load …

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 …

Using search engine data (SED) to forecast tourist flow is essential for management and
security warnings at tourist attractions. Existing prediction models cannot effectively handle …

Attention-based models have been very effective in identifying important lagged variables
for multivariate time series (MTS) forecasting applications. However, current attention-based …