Forecasting MDPI

19/11/2025

📢 Highly Cited Paper in !

"Evaluating the Potential of Copulas for Modeling Correlated Scenarios for Hydro, Wind, and Solar Energy"

✍️ Anderson M. Iung, Fernando L. Cyrino Oliveira, Andre L. M. Marcato and Guilherme A. A. Pereira

Proud to highlight this impactful contribution to renewable energy forecasting and correlated scenario modeling.

🔗 https://brnw.ch/21wXDnS

The increasing global adoption of variable renewable energy (VRE) sources has transformed the use of forecasting, scenario planning, and other techniques for managing their inherent generation uncertainty and interdependencies. What were once desirable enhancements are now fundamental requirements. ...

19/11/2025

📢 Highly Cited Paper in !

"Mode Decomposition Bi-Directional Long Short-Term Memory (BiLSTM) Attention Mechanism and Transformer (AMT) Model for Ozone (O3) Prediction in Johannesburg, South Africa"

✍️ Israel Edem Agbehadji and Ibidun Christiana Obagbuwa

Proud to share this impactful research advancing air quality prediction using state-of-the-art deep learning approaches.

🔗 https://brnw.ch/21wXDnU

This paper presents a model that combines mode decomposition approaches with a bi-directional long short-term memory (BiLSTM) attention mechanism and a transformer (AMT) to predict the concentration level of ozone (O3) in Johannesburg, South Africa. Johannesburg is a densely populated city and the i...

18/11/2025

📢 Highly Cited Paper in !

"Comparative Analysis of Supervised Learning Techniques for Forecasting PV Current in South Africa"

✍️ Ely Ondo Ekogha and Pius A. Owolawi

🔗 https://brnw.ch/21wXB3L

The fluctuations in solar irradiance and temperature throughout the year require an accurate methodology for forecasting the generated current of a PV system based on its specifications. The optimal technique must effectively manage rapid weather fluctuations while maintaining high accuracy in forec...

18/11/2025

📢 Highly Cited Paper in !

"Constructing Cybersecurity Stocks Portfolio Using AI"

✍️ Avishay Aiche and Gil Cohen

🔗 https://brnw.ch/21wXAZx

17/11/2025

📢 Highly Cited Paper in ForecastingMDPI!

"Does Google Analytics Improve the Prediction of Tourism Demand Recovery?"

✍️ Ilsé Botha & Andrea Saayman

🔗 https://brnw.ch/21wXzaq

Research shows that Google Trend indices can improve tourism-demand forecasts. Given the impact of the recent pandemic, this may prove to be an important predictor of tourism recovery in countries that are still struggling to recover, including South Africa. The purpose of this paper is firstly, to ...

17/11/2025

🎤 Invited Speaker Introduction: Dr. Katja Heinisch

We are pleased to welcome Dr. Katja Heinisch as our invited speaker today. Dr. Heinisch joined the Department of Macroeconomics in 2009 and has since established herself as a leading researcher in short-term forecasting and macroeconometric modelling.

She holds a diploma from Chemnitz University of Technology and the University of Strasbourg and earned her PhD from Osnabrück University. Dr. Heinisch has also gained extensive international research experience through her work at both the European Central Bank (ECB) and the International Monetary Fund (IMF).

Her research interests span a wide range of timely and impactful areas, including:
• Short-term forecasting and nowcasting
• Forecast combination
• Survey data
• Climate and international macroeconomics
• Macroeconometric modelling
• Applied time series econometrics

We’re excited to have her with us and look forward to her insights.

14/11/2025

📢 Highly Cited Paper in !

"Electricity Consumption Forecasting: An Approach Using Cooperative Ensemble Learning with SHapley Additive exPlanations"

✍️ Eduardo Luiz Alba, Gilson Adamczuk Oliveira, Matheus Henrique Dal Molin Ribeiro, and Érick Oliveira Rodrigues

This paper presents a novel method for electricity consumption forecasting using cooperative ensemble learning enhanced by SHapley Additive exPlanations (SHAP). The approach provides both high predictive accuracy and interpretability, offering valuable insights for energy management and decision-making.

🔗 https://brnw.ch/21wXuH0

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...

14/11/2025

📢 Highly Cited Paper in !

"Time-Series Interval Forecasting with Dual-Output Monte Carlo Dropout: A Case Study on Durian Exports"

✍️ Unyamanee Kummaraka and Patchanok Srisuradetchai

This paper presents an innovative approach to time-series interval forecasting using dual-output Monte Carlo dropout, applied to a real-world case study on durian exports. The work offers valuable insights for improving predictive reliability in complex forecasting tasks.

🔗https://brnw.ch/21wXuGC

Deep neural networks (DNNs) are prominent in predictive analytics for accurately forecasting target variables. However, inherent uncertainties necessitate constructing prediction intervals for reliability. The existing literature often lacks practical methodologies for creating predictive intervals,...

13/11/2025

📢 Highly Cited Paper in !

"Systematic Mapping Study of Sales Forecasting: Methods, Trends, and Future Directions"

✍️ Hamid Ahaggach, Lylia Abrouk & Eric Lebon

🔗 https://brnw.ch/21wXsul

In a dynamic business environment, the accuracy of sales forecasts plays a pivotal role in strategic decision making and resource allocation. This article offers a systematic review of the existing literature on techniques and methodologies used in forecasting, especially in sales forecasting across...

13/11/2025

📢 Highly Cited Paper in !

"Impact of PV and EV Forecasting in the Operation of a Microgrid"

✍️ Giampaolo Manzolini, Andrea Fusco, Domenico Gioffrè, Silvana Matrone, Riccardo Ramaschi, Marios Saleptsis, Riccardo Simonetti, Filip Sobic, Michael James Wood, Emanuele Ogliari and Sonia Leva

This paper explores how photovoltaic (PV) and electric vehicle (EV) forecasting influence the performance and optimization of microgrid operations, contributing valuable insights to the transition toward smarter and more sustainable energy systems.

🔗 https://brnw.ch/21wXstj

The electrification of the transport sector together with large renewable energy deployment requires powerful tools to efficiently use energy assets and infrastructure. In this framework, the forecast of electric vehicle demand and solar photovoltaic (PV) generation plays a fundamental role. This pa...

12/11/2025

📢 Highly Cited Paper in ForecastingMDPI!

📖 R&D Expenditures and Analysts’ Earnings Forecasts

✍️ Author: Taoufik Elkemali

This highly cited study examines the relationship between R&D spending and the accuracy of analysts’ earnings forecasts, offering valuable insights into financial forecasting and corporate innovation performance.

👉 Read the full article: https://brnw.ch/21wXqlK

&D

Previous research provides conflicting results regarding how R&D expenditures impact market value. Given that financial analysts are the primary intermediaries between companies and investors, our study focused on the impact of R&D-related uncertainty, growth, and information asymmetry associated on...

12/11/2025

📢 Highly Cited Paper in Forecasting!

📖 Forecasting Convective Storms Trajectory and Intensity by Neural Networks

✍️ Authors: Niccolò Borghi, Giorgio Guariso, and Matteo Sangiorgio

This highly cited study demonstrates how neural networks can be applied to accurately forecast the trajectory and intensity of convective storms, contributing valuable insights to weather prediction and climate risk analysis.

👉 Read the full article: https://brnw.ch/21wXqlE

Convective storms represent a dangerous atmospheric phenomenon, particularly for the heavy and concentrated precipitation they can trigger. Given their high velocity and variability, their prediction is challenging, though it is crucial to issue reliable alarms. The paper presents a neural network a...