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Forecasting MDPI Dr. Sonia Leva

Forecasting (ISSN 2571-9394) is an international and open access journal of all aspects of forecasting, published quarterly online by MDPI| IF: 2.3 Q2| Citescore: 5.8 Q1 | EiC: Prof.

📢 New Publication in ForecastingMDPIDiscover the latest research tackling a pressing global issue:📖 "Forecasting Youth U...
05/09/2025

📢 New Publication in ForecastingMDPI

Discover the latest research tackling a pressing global issue:
📖 "Forecasting Youth Unemployment Through Educational and Demographic Indicators: A Panel Time-Series Approach"

✍️ By Arsen Tleppayev and Saule Zeinolla

This study applies advanced panel time-series analysis to explore how education and demographic trends influence youth unemployment across regions — providing critical insights for policymakers and researchers alike.

🔗 Read the full article: https://brnw.ch/21wVvdX

Youth unemployment remains a pressing issue in many emerging economies, where educational disparities and demographic pressures interact in complex ways. This study investigates the links between higher-education enrolment, demographic structure and youth unemployment in eight developing countries f...

📢 New Publication in ForecastingMDPIWe’re excited to share the latest research:📖 "Exploiting Spiking Neural Networks for...
05/09/2025

📢 New Publication in ForecastingMDPI

We’re excited to share the latest research:
📖 "Exploiting Spiking Neural Networks for Click-Through Rate Prediction in Personalized Online Advertising Systems"

✍️ By Albin Uruqi and Iosif Viktoratos

This paper explores how Spiking Neural Networks (SNNs) can be leveraged to enhance CTR prediction in online advertising — pushing the boundaries of real-time, energy-efficient personalization.

🔗 Read the full article: https://brnw.ch/21wVv5B

This study explores the application of spiking neural networks (SNNs) for click-through rate (CTR) prediction in personalized online advertising systems, introducing a novel hybrid model, the Temporal Rate Spike with Attention Neural Network (TRA–SNN). By leveraging the biological plausibility and...

📢 New Publication in 📖 Probabilistic Projections of South Korea’s Population Decline and Subnational Dynamics✍️ By Jeon...
04/09/2025

📢 New Publication in

📖 Probabilistic Projections of South Korea’s Population Decline and Subnational Dynamics

✍️ By Jeongsoo Kim

🔗 Read the full article: https://brnw.ch/21wVt9v

📉 This study offers valuable insights into South Korea's future population trends using probabilistic models, with a focus on both national and regional levels.

This study adapts the United Nations’ methodology for national probabilistic population projections to subnational contexts. The Bayesian approach used by the UN addresses data collection complexities effectively. By applying hierarchical model assumptions, national projections can be extended to ...

📢 New Publication in 📖 Probabilistic Demand Forecasting in the Southeast Region of the Mexican Power System Using Machi...
04/09/2025

📢 New Publication in

📖 Probabilistic Demand Forecasting in the Southeast Region of the Mexican Power System Using Machine Learning Methods

✍️ By Ivan Itai Bernal Lara, Roberto Jair Lorenzo Diaz, Maria de los Ángeles Sánchez Galván, Jaime Robles García, Mohamed Badaoui, David Romero Romero, and Rodolfo Alfonso Moreno Flores

🔗 Read the full article here: https://brnw.ch/21wVt97

⚡️ This study explores machine learning techniques to enhance demand forecasting in power systems—critical for energy planning and grid reliability in Mexico.

This paper focuses on electricity demand forecasting and its uncertainty representation using a hybrid machine learning (ML) model in the eastern control area of southeastern Mexico. In this case, different sources of uncertainty are integrated by applying the Bootstrap method, which adds the charac...

📢 Highly Cited Paper in 📖 Forecasting Raw Material Yield in the Tanning Industry: A Machine Learning Approach ✍️ by Ism...
02/09/2025

📢 Highly Cited Paper in
📖 Forecasting Raw Material Yield in the Tanning Industry: A Machine Learning Approach
✍️ by Ismael Cristofer Baierle, Leandro Haupt, João Carlos Furtado, Eluza Toledo Pinheiro and Miguel Afonso Sellitto

🔗 Read more:

This study presents an innovative machine learning (ML) approach to predicting raw material yield in the leather tanning industry, addressing a critical challenge in production efficiency. Conducted at a tannery in southern Brazil, the research leverages historical production data to develop a predi...

📢 Highly Cited Paper in 📖Constructing Cybersecurity Stocks Portfolio Using AI✍️ by Avishay Aiche, Zvi Winer and Gil Coh...
02/09/2025

📢 Highly Cited Paper in
📖Constructing Cybersecurity Stocks Portfolio Using AI
✍️ by Avishay Aiche, Zvi Winer and Gil Cohen

🔗 Read more:

This study explores the application of artificial intelligence, specifically ChatGPT-4o, in constructing and managing a portfolio of cybersecurity stocks over the period from Q1 2018 to Q1 2024. Leveraging advanced machine learning models, fundamental analysis, sentiment analysis, and optimization t...

📢 Highly Cited Paper in 📖Assessing Meteorological Drought Patterns and Forecasting Accuracy with SPI and SPEI Using Mac...
01/09/2025

📢 Highly Cited Paper in
📖Assessing Meteorological Drought Patterns and Forecasting Accuracy with SPI and SPEI Using Machine Learning Models
✍️ by Bishal Poudel, Dewasis Dahal, Mandip Banjara and Ajay Kalra

🔗 Read more:

The rising frequency and severity of droughts requires accurate monitoring and forecasting to reduce the impact on water resources and communities. This study aims to investigate drought monitoring and categorization, while enhancing drought forecasting by using three machine learning models—Artif...

📢 Highly Cited Paper in 📖 Forecasting Short- and Long-Term Wind Speed in Limpopo Province Using Machine Learning and Ex...
01/09/2025

📢 Highly Cited Paper in
📖 Forecasting Short- and Long-Term Wind Speed in Limpopo Province Using Machine Learning and Extreme Value Theory
✍️ by Kgothatso Makubyane and Daniel Maposa

🔗 Read more:

This study investigates wind speed prediction using advanced machine learning techniques, comparing the performance of Vanilla long short-term memory (LSTM) and convolutional neural network (CNN) models, alongside the application of extreme value theory (EVT) using the r-largest order generalised ex...

📢 Highly Cited Paper in 📖Predicting Power Consumption Using Deep Learning with Stationary Wavelet✍️ by Majdi Frikha, Kh...
29/08/2025

📢 Highly Cited Paper in
📖Predicting Power Consumption Using Deep Learning with Stationary Wavelet
✍️ by Majdi Frikha, Khaled Taouil, Khaled Taouil and Faouzi Derbel

Explore advanced deep learning techniques for power consumption forecasting!

🔗 Read more:

Power consumption in the home has grown in recent years as a consequence of the use of varied residential applications. On the other hand, many families are beginning to use renewable energy, such as energy production, energy storage devices, and electric vehicles. As a result, estimating household ...

📢 Highly Cited Paper in !📖 Electricity Consumption Forecasting: An Approach Using Cooperative Ensemble Learning with SH...
29/08/2025

📢 Highly Cited Paper in !
📖 Electricity Consumption Forecasting: An Approach Using Cooperative Ensemble Learning with SHapley Additive exPlanations
✍️ by Eduardo Luiz Alba, Gilson Adamczuk Oliveira, Matheus Henrique Dal Molin Ribeiro, and Érick Oliveira Rodrigues

Discover cutting-edge methods in energy forecasting!

🔗 Read more:

Electricity expense management presents significant challenges, as this resource is susceptible to various influencing factors. In universities, the demand for this resource is rapidly growing with institutional expansion and has a significant environmental impact. In this study, the machine learnin...

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