Innovative machine learning approaches for complexity in economic forecasting and SME growth: A comprehensive review by Mustafa I. Al-Karkhi, Grzegorz Rza̧dkowski

“…Economic forecasting and small and medium-sized enterprises (SMEs) growth prediction have become essential tools for guiding policy, business strategy, and economic development in an increasingly data-driven world. …”
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Nonlinear time domain and multi-scale frequency domain feature fusion for time series forecasting by Kejiang Xiao, Yefeng Li, Yaning Dong, Wenqi Yang, Binting Yao, Liang Chen

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Uncertainty-Aware Earthquake Forecasting Using a Bayesian Neural Network with Elastic Weight Consolidation by Changchun Liu, Yuting Li, Huijuan Gao, Lin Feng, Xinqian Wu

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Adequacy Evaluation of Power System Ramping Capability Based on Net Load Forecast Error Statistics by Zhongfei CHEN, Yue ZHAO, Qiuna CAI, Qiaoyu ZHANG, Zelin WANG, Xiaojuan DAI, Yuguo CHEN

“…Finally, an example analysis is carried out based on the data of forty historical operating days and four typical days in Guangdong to validate the effectiveness of the proposed adequacy evaluation method. …”
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Enhancing Time Series Product Demand Forecasting With Hybrid Attention-Based Deep Learning Models by Xuguang Zhang, Pan Li, Xu Han, Yongbin Yang, Yiwen Cui

“…This research contributes to the growing body of work on deep learning for time series analysis and offers practical implications for improving demand forecasting in retail and supply chain management.…”
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Forecasting loan, deferred rate and customer segmentation in banking industry: A computational intelligence approach by Mahtab Vasheghani, Ebrahim Nazari Farokhi, Behrooz Dolatshah

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Ocean Currents Velocity Hindcast and Forecast Bias Correction Using a Deep-Learning Approach by Ali Muhamed Ali, Hanqi Zhuang, Yu Huang, Ali K. Ibrahim, Ali Salem Altaher, Laurent M. Chérubin

“…In this study, we present a machine learning-based three-dimensional velocity bias correction method derived from historical observations that applies to both hindcast and forecast. Our approach is based on the modification of an existing deep learning model, called U-Net, designed specifically for image segmentation analysis in the biomedical field. …”
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Transformer Versus LSTM: A Comparison of Deep Learning Models for Karst Spring Discharge Forecasting by Anna Pölz, Alfred Paul Blaschke, Jürgen Komma, Andreas H. Farnleitner, Julia Derx

“…This study evaluates the performance of the Transformer in forecasting spring discharges for up to 4 days. We compare it to the Long Short‐Term Memory (LSTM) Neural Network and a common baseline model on a well‐studied Austrian karst spring (LKAS2) with an extensive hourly database. …”
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Optimizing Precipitation Forecasting and Agricultural Water Resource Allocation Using the Gaussian-Stacked-LSTM Model by Maofa Wang, Bingcheng Yan, Yibo Zhang, Lu Zhang, Pengcheng Wang, Jingjing Huang, Weifeng Shan, Haijun Liu, Chengcheng Wang, Yimin Wen

“…Additionally, we demonstrate the practical benefits of precipitation forecasts in optimizing water resource allocation. …”
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Trend Detection and Forecasting of LST in Tabriz City using the Non-parametric Mann-Kendall and NNAR by Mohammad Ali Koushesh Vatan, Akbar Asghari Zamani, Shahrivar Rostaei

“…Aim: This study aims to analyze and forecast the LST during the summer season in Tabriz by 2030. …”
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Synergizing TabNet and SHAP for PM10 Forecasting: Insights From Makkah, Saudi Arabia by Abdulrazak H. Almaliki, Afaq Khattak

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Optimizing Lightweight Recurrent Networks for Solar Forecasting in TinyML: Modified Metaheuristics and Legal Implications by Gradimirka Popovic , Zaklina Spalevic , Luka Jovanovic , Miodrag Zivkovic , Lazar Stosic , Nebojsa Bacanin 

“…The discussion provided in this manuscript also includes the legal framework for renewable energy forecasting, its integration, and the policy implications of establishing a decentralized and cost-effective forecasting system.…”
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Characteristics and Forecasting of Rocky Desertification Dynamics in the Pearl River Source Region from 1990 to 2030 by Haojun Sun, Shaoyun Zhang, Songyang He, Zecheng Liu

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Optimizing Air Pollution Forecasting Across Temporal Scales: A Case Study in Salamanca, Mexico by Francisco-Javier Moreno-Vazquez, Felipe Trujillo-Romero, Amanda Enriqueta Violante Gavira

“…Air pollution forecasting is essential for understanding environmental patterns and mitigating health risks, especially in urban areas. …”
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Building electrical consumption patterns forecasting based on a novel hybrid deep learning model by Nasser Shahsavari-Pour, Azim Heydari, Farshid Keynia, Afef Fekih, Aylar Shahsavari-Pour

“…This paper addresses the problem of accurate energy forecasting by proposing an intelligent hybrid model that integrates advanced feature selection, signal decomposition, and deep learning techniques. …”
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Forecasting Residential Energy Consumption with the Use of Long Short-Term Memory Recurrent Neural Networks by Zurisaddai Severiche-Maury, Carlos Eduardo Uc-Rios, Wilson Arrubla-Hoyos, Dora Cama-Pinto, Juan Antonio Holgado-Terriza, Miguel Damas-Hermoso, Alejandro Cama-Pinto

“…Additionally, the predictive performance was strong, with MSE values of 1.0464 × 10⁻⁶ for usage time, 0.0163 for individual consumption, and 0.0168 for total consumption. The analysis of scatter plots and residuals revealed a high degree of correspondence between predicted and actual values, validating the model’s accuracy and reliability in energy forecasting. …”
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Prediction Analysis for Business To Business (B2B) Sales of Telecommunication Services using Machine Learning Techniques by Oryza Wisesa, Andi Andriansyah, Osamah Khalaf

“…In most cases, business highly relies on information as well as demand forecast of the sales trends. This research uses B2B sales data for analysis. …”
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Prediction and analysis of China’s coastal marine economy: an innovative grey model with the best-matching data-preprocessing techniques by Zerong Wang, Zhijian Cai, Yao Li

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Enhancing corn industry sustainability through deep learning hybrid models for price volatility forecasting. by Chengjin Yang, Yanzhong Zhai, Zehua Liu

“…The dataset utilized in this study was sourced from the BREC Agricultural Big Data platform, ensuring the reliability and accuracy of the corn price data for our analysis. This study utilizes price data from China's five major corn-producing regions as a case study to demonstrate the efficacy of the proposed model in corn price forecasting. …”
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A Hybrid Strategy-Improved SSA-CNN-LSTM Model for Metro Passenger Flow Forecasting by Jing Liu, Qingling He, Zhikun Yue, Yulong Pei

“…The ISSA-CNN-LSTM model is suitable for the precise prediction of passenger flow at different types of subway stations, providing theoretical and data support for subway station passenger density and trend forecasting, passenger organization and management, risk emergency response, and the improvement of service quality and operational safety.…”
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