Forecasting MDPI

23/12/2025

🎄✨ Happy New Year from ForecastingMDPI

As we welcome the new year, we would like to extend our sincere thanks to our authors, reviewers, editors, and readers for being part of the Forecasting community. Your contributions and support continue to drive high-quality research and innovation in forecasting science.

We wish you a successful year ahead filled with impactful research, collaboration, and new opportunities. 🎉🌟

23/12/2025

📢 Highly Cited in

📖 A Markov Switching Autoregressive Model with Time-Varying Parameters

✍️ Syarifah Inayati, Nur Iriawan and Irhamah

This study introduces a Markov switching autoregressive model with time-varying parameters, providing a robust approach for dynamic forecasting in complex systems. A valuable resource for researchers and practitioners in statistics, econometrics, and data science.

🔗 Read the full study: https://brnw.ch/21wYAlU

This study showcased the Markov switching autoregressive model with time-varying parameters (MSAR-TVP) for modeling nonlinear time series with structural changes. This model enhances the MSAR framework by allowing dynamic parameter adjustments over time. Parameter estimation uses maximum likelihood ...

23/12/2025

📢 Highly Cited in

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

✍️ Hamid Ahaggach, Lylia Abrouk and Eric Lebon

This study provides a comprehensive overview of sales forecasting research, highlighting key methods, current trends, and potential future directions.

🔗 Read the full study: https://brnw.ch/21wYAlI

22/12/2025

📢 New Special Issue in

"Forecasting Impacts of Air Pollution and Hydro-Meteorological Extremes: Models, Methods, and Applications"

Guest Editors: Dr. Chibuike Chiedozie Ibebuchi and Dr. Richard Damoah

We are pleased to announce that this Special Issue is now open for submissions! Researchers are invited to contribute studies on forecasting methods, models, and practical applications related to air pollution and extreme hydro-meteorological events.

📆 Submission Deadline: 31 December 2026

🔗 Learn more & submit: https://brnw.ch/21wYyvX

22/12/2025

📢 Highly Cited in Forecasting

📖 Global Solar Radiation Forecasting Based on Hybrid Model with Combinations of Meteorological Parameters: Morocco Case Study

✍️ Brahim Belmahdi, Mohamed Louzazni, Mousa Marzband, and Abdelmajid El Bouardi

☀️ This study demonstrates how hybrid forecasting models combining multiple meteorological parameters can improve solar radiation predictions, supporting better renewable energy planning and management.

🔗 https://brnw.ch/21wYyvQ

19/12/2025

📢 The 1st International Online Conference on Forecasting (IOCFC 2026)
🗓️ 21–22 September 2026 | Online
🎤 Present Your Work at IOCFC 2026
📝 Abstract submission is now open!

🌍 Join the global forecasting community and contribute to advances in forecasting methods, applications, and decision-making.

🔗 Submit your abstract here: https://brnw.ch/21wYuFZ

19/12/2025

📢 Recent Publication in

📘 A Wavelet–Attention–Convolution Hybrid Deep Learning Model for Accurate Short-Term Photovoltaic Power Forecasting

✍️ Kaoutar Ait Chaoui, Hassan EL Fadil, Oumaima Choukai and Oumaima Ait Omar

🔗 https://brnw.ch/21wYuAf

🔍 This work proposes an advanced hybrid deep learning model to improve the accuracy and reliability of short-term photovoltaic power forecasting.

18/12/2025

📢 Highly Cited in !

📖 Performance Analysis of Statistical, Machine Learning and Deep Learning Models in Long-Term Forecasting of Solar Power Production

✍️ Ashish Sedai, Rabin Dhakal, Shishir Gautam, Anibesh Dhamala, Argenis Bilbao, Qin Wang, Adam Wigington and Suhas Pol

☀️ This highly cited work evaluates a range of forecasting models for solar power production, providing key insights to improve renewable energy planning and grid management.

👉 https://brnw.ch/21wYsD3

18/12/2025

📢 Recent Publication in !

📘 Exploiting Spiking Neural Networks for Click-Through Rate Prediction in Personalized Online Advertising Systems

✍️ By Albin Uruqi and Iosif Viktoratos

🧠 This study explores the use of spiking neural networks to improve click-through rate prediction, paving the way for more effective and personalized online advertising.

🔗 https://brnw.ch/21wYsCH

17/12/2025

📢 Recent publication in !

📘 Optimizing Credit Risk Prediction for Peer-to-Peer Lending Using Machine Learning

✍️ Lyne Imene Souadda, Ahmed Rami Halitim, Billel Benillesm, José Manuel Oliveira and Patrícia Ramos

📊 This study showcases how machine learning techniques can significantly enhance credit risk assessment in peer-to-peer lending platforms.

🔗 https://brnw.ch/21wYqQx

Hyperparameter optimization (HPO) is critical for enhancing the predictive performance of machine learning models in credit risk assessment for peer-to-peer (P2P) lending. This study evaluates four HPO methods, Grid Search, Random Search, Hyperopt, and Optuna, across four models, Logistic Regression...

17/12/2025

📢 Highly Cited in !

📖 On Forecasting Cryptocurrency Prices: A Comparison of Machine Learning, Deep Learning, and Ensembles

✍️ Kate Murray, Andrea Rossi, Diego Carraro and Andrea Visentin

📈 This highly cited study provides a comprehensive comparison of advanced forecasting approaches for cryptocurrency markets, offering valuable insights for researchers and practitioners alike.

👉 https://brnw.ch/21wYqQ7

Traders and investors are interested in accurately predicting cryptocurrency prices to increase returns and minimize risk. However, due to their uncertainty, volatility, and dynamism, forecasting crypto prices is a challenging time series analysis task. Researchers have proposed predictors based on ...

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, ...