Forecasting MDPI

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...

11/11/2025

📢 Highly Cited Paper in

"Forecasting Thailand’s Transportation CO₂ Emissions: A Comparison among Artificial Intelligent Models

✍️Thananya Janhuaton, Vatanavongs Ratanavaraha and Sajjakaj Jomnonkwao

🔗 https://brnw.ch/21wXobJ

Transportation significantly influences greenhouse gas emissions—particularly carbon dioxide (CO2)—thereby affecting climate, health, and various socioeconomic aspects. Therefore, in developing and implementing targeted and effective policies to mitigate the environmental impacts of transportati...

11/11/2025

📢 Highly Cited Paper in !

"Effective Natural Language Processing Algorithms for Early Alerts of Gout Flares from Chief Complaints"

✍️ Lucas Lopes Oliveira, Xiaorui Jiang, Aryalakshmi Nellippillipathil Babu, Poonam Karajagi & Alireza Daneshkhah

🔗 https://brnw.ch/21wXobF

Early identification of acute gout is crucial, enabling healthcare professionals to implement targeted interventions for rapid pain relief and preventing disease progression, ensuring improved long-term joint function. In this study, we comprehensively explored the potential early detection of gout ...

10/11/2025

📢 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/21wXmrT

This study explores how machine learning and statistical forecasting can improve pharmaceutical sales predictions, offering valuable insights for the healthcare and business sectors.

10/11/2025

📢 Highly Cited Paper in

⚡Forecasting the Occurrence of Electricity Price Spikes: A Statistical-Economic Investigation Study

✍️ Manuel Zamudio López, Hamidreza Zareipour, Mike Quashie

🔗 https://brnw.ch/21wXmrH

This study provides valuable insights into predicting electricity price spikes using statistical and economic approaches—important for energy markets and policy planning.

This research proposes an investigative experiment employing binary classification for short-term electricity price spike forecasting. Numerical definitions for price spikes are derived from economic and statistical thresholds. The predictive task employs two tree-based machine learning classifiers ...

07/11/2025

📢 Highly Cited Paper in !

"Can Denoising Enhance Prediction Accuracy of Learning Models? A Case of Wavelet Decomposition Approach"

✍️ C. Tamilselvi, Md Yeasin, Ranjit Kumar Paul and Amrit Kumar Paul

This study examines the impact of signal denoising through wavelet decomposition on the predictive accuracy of learning models — offering valuable insights into improving data-driven forecasting techniques.

🔗 https://brnw.ch/21wXi7x

Denoising is an integral part of the data pre-processing pipeline that often works in conjunction with model development for enhancing the quality of data, improving model accuracy, preventing overfitting, and contributing to the overall robustness of predictive models. Algorithms based on a combina...

07/11/2025

📢 Highly Cited Paper in !

"Advancements in Downscaling Global Climate Model Temperature Data in Southeast Asia: A Machine Learning Approach"

✍️ Teerachai Amnuaylojaroen

This research presents a machine learning–based framework to improve the spatial resolution and accuracy of global climate model temperature data across Southeast Asia — contributing to more precise regional climate projections and impact assessments.

🔗https://brnw.ch/21wXi7v

Southeast Asia (SEA), known for its diverse climate and broad coastal regions, is particularly vulnerable to the effects of climate change. The purpose of this study is to enhance the spatial resolution of temperature projections over Southeast Asia (SEA) by employing three machine learning methods:...

06/11/2025

📢 Highly Cited Paper in !

"Macroeconomic Predictions Using Payments Data and Machine Learning"

✍️ Authors: James T. E. Chapman and Ajit Desai

This study explores how payments data and machine learning techniques can enhance macroeconomic forecasting, offering new insights into real-time economic trend prediction and policy analysis.

🔗 https://brnw.ch/21wXg6b

This paper assesses the usefulness of comprehensive payments data for macroeconomic predictions in Canada. Specifically, we evaluate which type of payments data are useful, when they are useful, why they are useful, and whether machine learning (ML) models enhance their predictive value. We find pay...

06/11/2025

📢 Highly Cited Paper in !

“Predictive Analytics of Air Temperature in Alaskan Permafrost Terrain Leveraging Two-Level Signal Decomposition and Deep Learning”

✍️ Aymane Ahajjam, Jaakko Putkonen, Emmanuel Chukwuemeka, Robert Chance & Timothy J. Pasch

🔗https://brnw.ch/21wXfXR

Local weather forecasts in the Arctic outside of settlements are challenging due to the dearth of ground-level observation stations and high computational costs. During winter, these forecasts are critical to help prepare for potentially hazardous weather conditions, while in spring, these forecasts...

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 ...