Forecasting MDPI

05/11/2025

📢 Highly Cited Paper 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

This study explores the performance of classical and deep learning models (ARIMA and LSTM) in predicting traffic flow, offering valuable insights for urban mobility management and intelligent transport systems.

🔗 Read here: https://brnw.ch/21wXec0

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 ...

05/11/2025

📢 Highly Cited Paper in !

“Decompose and Conquer: Time Series Forecasting with Multiseasonal Trend Decomposition Using Loess”

✍️ By: Amirhossein Sohrabbeig, Omid Ardakanian and Petr Musilek

This study presents a robust approach to time series forecasting through multiseasonal trend decomposition using LOESS, enhancing predictive performance across complex datasets.

🔗 Read the full paper: https://brnw.ch/21wXe8W

Over the past few years, there has been growing attention to the Long-Term Time Series Forecasting task and solving its inherent challenges like the non-stationarity of the underlying distribution. Notably, most successful models in this area use decomposition during preprocessing. Yet, much of the ...

04/11/2025

📢 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 & Érick Oliveira Rodrigues

🔗 Read the full paper here: https://brnw.ch/21wXbI3

04/11/2025

📢 Highly Cited Paper in Forecasting

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

✍️ Panagiotis Eleftheriadis, Spyridon Giazitzis, Sonia Leva & Emanuele Ogliari

The study provides a comprehensive overview of data-driven approaches for estimating the state of charge of lithium-ion batteries, a key challenge in energy storage and management.

🔗 Read the full paper here: https://brnw.ch/21wXbHB

In recent years, there has been a noticeable shift towards electric mobility and an increasing emphasis on integrating renewable energy sources. Consequently, batteries and their management have been prominent in this context. A vital aspect of the BMS revolves around accurately determining the batt...

03/11/2025

📢 Highly Cited Paper in !

📖 Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins

✍️ Author: Apostolos Ampountolas

This paper provides a comprehensive comparison of machine learning, hybrid, and deep learning models for forecasting financial markets and cryptocurrencies.

🔗 Read the full article: https://brnw.ch/21wXa3j

This study analyzes the transmission of market uncertainty on key European financial markets and the cryptocurrency market over an extended period, encompassing the pre-, during, and post-pandemic periods. Daily financial market indices and price observations are used to assess the forecasting model...

03/11/2025

📢 Highly Cited Paper in !

📖 Large Language Models: Their Success and Impact

✍️ Authors: Spyros Makridakis, Fotios Petropoulos & Yanfei Kang

This paper examines the success, applications, and implications of Large Language Models (LLMs), exploring their impact on forecasting, AI research, and real-world decision-making.

🔗 Read the full article: https://brnw.ch/21wXa2X

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, ...

31/10/2025

📢 Highly Cited Paper in !

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

✍️ By Florin Aliu, Jiří Kučera & Simona Hašková

🔗 Read here: https://brnw.ch/21wX6ak

The Russian invasion of Ukraine on 24 February 2022 accelerated agricultural commodity prices and raised food insecurities worldwide. Ukraine and Russia are the leading global suppliers of wheat, corn, barley and sunflower oil. For this purpose, we investigated the relationship among these four agri...

31/10/2025

📢 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

🔗 Read here: https://brnw.ch/21wX61v

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...

30/10/2025

📢 Highly Cited Paper in Forecasting

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

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

Proud to share that this paper has been recognized as a Highly Cited Paper in Forecasting (MDPI) — a great contribution to the fields of solar power forecasting, deep learning, and transfer learning.

🔗 Read the full paper: https://brnw.ch/21wX4d9

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...

30/10/2025

📢 Highly Cited Paper in !

📖 Day Ahead Electric Load Forecast: A Comprehensive LSTM-EMD Methodology and Several Diverse Case Studies

✍️ By Michael Wood, Emanuele Ogliari, Alfredo Nespoli, Travis Simpkins & Sonia Leva

🔗 Read here: https://brnw.ch/21wX4co

Optimal behind-the-meter energy management often requires a day-ahead electric load forecast capable of learning non-linear and non-stationary patterns, due to the spatial disaggregation of loads and concept drift associated with time-varying physics and behavior. There are many promising machine le...

29/10/2025

📢 Highly Cited Paper in !

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

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

This study offers a comprehensive comparison of ML, DL, and ensemble approaches for predicting cryptocurrency price movements — highlighting the potential of AI-driven forecasting in volatile financial markets.

🔗 Read the full paper: https://brnw.ch/21wX2is

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 ...

29/10/2025

📢 Highly Cited Paper in !

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

✍️ By: Ashish Sedai et al.

This study provides an in-depth comparison of statistical, ML, and DL models for long-term solar power forecasting — offering valuable insights for renewable energy prediction and optimization.

👉 Read more: https://brnw.ch/21wX2ia

The Machine Learning/Deep Learning (ML/DL) forecasting model has helped stakeholders overcome uncertainties associated with renewable energy resources and time planning for probable near-term power fluctuations. Nevertheless, the effectiveness of long-term forecasting of renewable energy resources u...