Forecasting MDPI

10/12/2025

📢 Highly Cited Paper in !

📖 Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview

✍️ By Panagiotis Eleftheriadis, Spyridon Giazitzis, Sonia Leva and Emanuele Ogliari

This overview highlights key data-driven approaches for accurate battery state-of-charge estimation, offering valuable insights for advancing modern energy storage systems.

👉 https://brnw.ch/21wYegr

10/12/2025

📢 Recent Publication in !

📘 Multifeature-Driven Multistep Wind Speed Forecasting Using NARXR and Modified VMD Approaches

✍️ By Rose Ellen Macabiog and Jennifer Dela Cruz

🔗 https://brnw.ch/21wYegj

The global demand for clean and sustainable energy has driven the rapid growth of wind power. However, wind farm managers face the challenge of forecasting wind power for efficient power generation and management. Accurate wind speed forecasting (WSF) is vital for predicting wind power; yet, the var...

09/12/2025

📢 Highly Cited Paper in !

📖 A Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India

✍️ By Ansari Saleh Ahmar, Pawan Kumar Singh, R. Ruliana, Alok Kumar Pandey and Stuti Gupta

👉 https://brnw.ch/21wYcjo

This highly cited work showcases impactful time-series forecasting research with real-world relevance for food security and agricultural planning.

The agriculture sector plays an essential function within the Indian economic system. Foodgrains provide almost all the calories and proteins. This paper aims to compare ARIMA, SutteARIMA, Holt-Winters, and NNAR models to recommend an effective model to predict foodgrains production in India. The ex...

09/12/2025

🌟 Call for Abstracts Now Open! 🌟

We are excited to invite you to participate in the 1st International Online Conference on Forecasting (IOCFC 2026), happening 21–22 September 2026.

Whether you work in climate forecasting, economic prediction, AI and machine learning, risk analysis, or any other forecasting-related field, this conference is the perfect place to share your work and connect with experts worldwide — all from the comfort of your home!

📅 Event Date: 21–22 September 2026💻 Format: Online

📚 Submit your abstract today!

👉 https://brnw.ch/21wYciZ

Feel free to share this with colleagues who may be interested!

08/12/2025

📢 Highly Cited Paper in !

"Applying Machine Learning and Statistical Forecasting Methods for Enhancing Pharmaceutical Sales Predictions"

✍️ Konstantinos P. Fourkiotis and Athanasios Tsadiras

🔗 https://brnw.ch/21wYb95

This impactful study highlights how advanced forecasting and ML techniques can support more accurate decision-making in the pharmaceutical industry.

08/12/2025

📢 Recent Publication in !

"Comparative Analysis of Physics-Guided Bayesian Neural Networks for Uncertainty Quantification in Dynamic Systems"

✍️ Xinyue Xu and Julian Wang

🔗 https://brnw.ch/21wYb8G

This study offers valuable insights into how physics-guided BNNs can improve uncertainty quantification and enhance modeling reliability in complex dynamic systems.

Uncertainty quantification (UQ) is critical for modeling complex dynamic systems, ensuring robustness and interpretability. This study extends Physics-Guided Bayesian Neural Networks (PG-BNNs) to enhance model robustness by integrating physical laws into Bayesian frameworks. Unlike Artificial Neural...

05/12/2025

📢 Highly Cited Paper in !

📖 Agricultural Commodities in the Context of the Russia-Ukraine War: Evidence from Corn, Wheat, Barley, and Sunflower Oil

✍️ By Florin Alius, Jiří Kučera and Simona Hašková

This study analyzes the impact of the Russia-Ukraine conflict on key agricultural commodity markets, providing evidence-based insights for policymakers and market participants.

👉 Read more: https://brnw.ch/21wY7fB

05/12/2025

📢 New in !

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

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

This study explores the use of copula models to capture correlations among hydro, wind, and solar energy scenarios, enhancing the accuracy of renewable energy forecasting and planning.

🔗 https://brnw.ch/21wYcoF

04/12/2025

📢 New in !

📘 White Noise and Its Misapplications: Impacts on Time Series Model Adequacy and Forecasting

✍️ By Hossein Hassani, Leila Marvian Mashhad, Manuela Royer-Carenzi, Mohammad Reza Yeganegi & Nadejda Komendantova

This paper explores common misapplications of white noise in time series modeling and highlights their impacts on model adequacy and forecasting accuracy.

🔗 Read here:

This paper contributes significantly to time series analysis by discussing the empirical properties of white noise and their implications for model selection. This paper illustrates the ways in which the standard assumptions about white noise typically fail in practice, with a special emphasis on st...

04/12/2025

📢 Highly Cited Paper in !

📖 A Hybrid Model for Multi-Day-Ahead Electricity Price Forecasting considering Price Spikes

✍️ By: Daniel Manfre Jaimes, Manuel Zamudio López, Hamidreza Zareipour and Mike Quashie

This study presents a hybrid forecasting model that improves accuracy in predicting electricity prices, particularly during extreme price spikes, providing valuable insights for energy market planning.

👉 Read more:

This paper proposes a new hybrid model to forecast electricity market prices up to four days ahead. The components of the proposed model are combined in two dimensions. First, on the “vertical” dimension, long short-term memory (LSTM) neural networks and extreme gradient boosting (XGBoost) model...

03/12/2025

📢 Highly Cited Paper in !

📖 A Day-Ahead Photovoltaic Power Prediction via Transfer Learning and Deep Neural Networks

✍️ By: Seyed Mahdi Miraftabzadeh, Cristian Giovanni Colombo, Michela Longo & Federica Foiadelli

⚡ Boosts solar forecasting accuracy even with minimal data!

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Climate change and global warming drive many governments and scientists to investigate new renewable and green energy sources. Special attention is on solar panel technology, since solar energy is considered one of the primary renewable sources and solar panels can be installed in domestic neighborh...

03/12/2025

📢 New Publication in !

📘 The MECOVMA Framework: Implementing Machine Learning Under Macroeconomic Volatility for Marketing Predictions

✍️ By Manuel Muth

📊 Boosts marketing prediction accuracy in volatile economic conditions!

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The methodological framework introduced in this paper, MECOVMA, is a novel framework that guides the application of Machine Learning specifically for marketing predictions within volatile macroeconomic environments. MECOVMA has been developed in response to the identified gaps displayed by existing....