Forecasting MDPI

16/12/2025

📢 Highly Cited in !

📖 Large Language Models: Their Success and Impact

✍️ Spyros Makridakis, Fotios Petropoulos and Yanfei Kang

🤖 This highly cited article provides a comprehensive discussion of the success of large language models and their growing impact on forecasting, analytics, and decision-making across disciplines.

👉 https://brnw.ch/21wYp52

ChatGPT, a state-of-the-art large language model (LLM), is revolutionizing the AI field by exhibiting humanlike skills in a range of tasks that include understanding and answering natural language questions, translating languages, writing code, passing professional exams, and even composing poetry, ...

16/12/2025

📢 Recent publication in !

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

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

📊 This article explores how machine learning methods can enhance probabilistic demand forecasting in power systems, offering valuable insights for energy planning and decision-making.

🔗 https://brnw.ch/21wYp4Q

15/12/2025

📢 Recent publication in !

📘 Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning

✍️ Kamran Hassanpouri Baesmat, Zeinab Farrokhi, Grzegorz Chmaj and Emma E. Regentova

🔗 https://brnw.ch/21wYn8M

This paper presents a robust, cloud-enabled forecasting framework that improves energy demand prediction through advanced machine learning and model redundancy.

15/12/2025

📢 Recent publication in !

📘 Improvement on Forecasting of Propagation of the COVID-19 Pandemic through Combining Oscillations in ARIMA Models

✍️ By Eunju Hwang

🔗 https://brnw.ch/21wYn8c

This study enhances pandemic forecasting accuracy by incorporating oscillatory behavior into ARIMA models, offering valuable insights for public health planning.

Daily data on COVID-19 infections and deaths tend to possess weekly oscillations. The purpose of this work is to forecast COVID-19 data with partially cyclical fluctuations. A partially periodic oscillating ARIMA model is suggested to enhance the predictive performance. The model, optimized for impr...

12/12/2025

📢 Highly Cited in !

📖 Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima Junction

✍️ By Vienna N. Katambire, Richard Musabe, Alfred Uwitonze, and Didacienne Mukanyiligira

👉 https://brnw.ch/21wYiZn

This impactful study demonstrates how combining classical and deep learning models can significantly improve urban traffic flow prediction.

Traffic operation efficiency is greatly impacted by the increase in travel demand and the increase in vehicle ownership. The continued increase in traffic demand has rendered the importance of controlling traffic, especially at intersections. In general, the inefficiency of traffic scheduling leads ...

12/12/2025

📢 Recent Publication in !

📘 Wind Speed Forecasting with Differentially Evolved Minimum-Bandwidth Filters and Gated Recurrent Units

✍️ By Khathutshelo Steven Sivhugwana and Edmore Ranganai

🔗 https://bit.ly/4s3kqcS

This study presents an innovative hybrid approach that enhances wind speed prediction accuracy—supporting more reliable renewable energy systems.

Wind data are often cyclostationary due to cyclic variations, non-constant variance resulting from fluctuating weather conditions, and structural breaks due to transient behaviour (due to wind gusts and turbulence), resulting in unreliable wind power supply. In wavelet hybrid forecasting, wind predi...

11/12/2025

📢 Recent Published in !

📖 Day-Ahead Energy Price Forecasting with Machine Learning: Role of Endogenous Predictors

✍️ By Chibuike Chiedozie Ibebuchi

👉 https://bit.ly/48TXMdQ

This publication provides an in-depth analysis of how carefully selected endogenous predictors can enhance the accuracy of day-ahead energy price forecasting using machine learning models. A valuable contribution for professionals and researchers in energy markets and data-driven forecasting.

11/12/2025

📢 Highly Cited in !

📖 Deep Learning Models for Bitcoin Prediction Using Hybrid Approaches with Gradient-Specific Optimization

✍️ By Amina Ladhari and Heni Boubaker

👉 https://brnw.ch/21wYgQ5

This work highlights how hybrid deep learning methods can significantly enhance the accuracy of cryptocurrency market forecasting.

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://bit.ly/4rYJ3Y0

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!