Forecasting MDPI

15/01/2026

📢 New Publication in

📖 A Novel k-Nearest Neighbors Approach for Forecasting Sub-Seasonal Precipitation at Weather Observing Stations

✍️ Sean Guidry Stanteen, Jianzhong Su, Paul Flanagan, Xunchang John Zhang

This study introduces a k-Nearest Neighbors method to enhance sub-seasonal precipitation predictions at weather observing stations.

🔗 https://brnw.ch/21wZ6K6

15/01/2026

📢 New Publication in

📖 A New Loss Function for Enhancing Peak Prediction in Time Series Data with High Variability

A new loss function is proposed to enhance peak prediction in time series data with high variability, offering improved accuracy for challenging datasets.

✍️ Mahan Hajiabbasi Somehsaraie, Soheyla Tofighi, Zhaoan Wang, Jun Wang and Shaoping Xiao

🔗 https://brnw.ch/21wZ6DP

14/01/2026

📘 Must Read in !

📖 Exploring the Role of Online Courses in COVID-19 Crisis Management in the Supply Chain Sector—Forecasting Using Fuzzy Cognitive Map (FCM) Models

Discover how FCM models can forecast the impact of online courses on supply chain crisis management during COVID-19.

🔗 Read the full article: https://brnw.ch/21wZ53k

14/01/2026

📘 Must Read in !

📖 Evaluating Wind Speed Forecasting Models: A Comparative Study of CNN, DAN2, Random Forest and XGBOOST in Diverse South African Weather Conditions

Compare the performance of advanced forecasting models for wind speed across different South African weather conditions.

🔗 Read the full article:https://brnw.ch/21wZ52x

13/01/2026

📘 Must Read in !

📖 Electricity Consumption Forecasting: An Approach Using Cooperative Ensemble Learning with SHapley Additive exPlanations

Discover how cooperative ensemble learning and SHAP can improve electricity consumption forecasting.

🔗 Read the full article: https://brnw.ch/21wZ36r

Electricity expense management presents significant challenges, as this resource is susceptible to various influencing factors. In universities, the demand for this resource is rapidly growing with institutional expansion and has a significant environmental impact. In this study, the machine learnin...

13/01/2026

📘 Must Read in !

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

Explore how NLP algorithms can provide early alerts for gout flares using patient chief complaints.

🔗 Read the full article: https://brnw.ch/21wZ36m

13/01/2026

We are pleased to announce the appointment of Prof. Dr. Shaolong Sun as the Section Editor-in-Chief of the “AI Forecasting” Section.

Professor at Xi'an Jiaotong University, China, Prof. Dr. Sun has published over 100 research papers and has been named among Stanford University's World's Top 2% Scientists list.

"My goal as Section Editor-in-Chief is to strengthen the scientific influence of Forecasting by encouraging cross-disciplinary dialogue, supporting innovative Special Issues, and maintaining a rigorous yet constructive peer-review process that ensures both quality and impact." - Prof. Dr. Shaolong Sun.

See the full interview: https://brnw.ch/21wZ2QO

12/01/2026

📘 Must Read in !

📖 Distribution Prediction of Decomposed Relative EVA Measure with Levy-Driven Mean-Reversion Processes: The Case of an Automotive Sector of a Small Open Economy

Explore advanced forecasting of EVA measures in the automotive sector using Levy-driven mean-reversion processes.

🔗 Read the full article: https://brnw.ch/21wZ1ew

The paper is focused on predicting the financial performance of a small open economy with an automotive industry with an above-standard share. The paper aims to predict the probability distribution of the decomposed relative economic value-added measure of the automotive production sector NACE 29 in...

12/01/2026

📘 Must Read in !

📖 Does Google Analytics Improve the Prediction of Tourism Demand Recovery?

Discover how Google Analytics data can enhance forecasting of tourism demand recovery.

🔗 Read the full article: https://brnw.ch/21wZ1ee

Research shows that Google Trend indices can improve tourism-demand forecasts. Given the impact of the recent pandemic, this may prove to be an important predictor of tourism recovery in countries that are still struggling to recover, including South Africa. The purpose of this paper is firstly, to ...

09/01/2026

📘 Must Read in !

📖 Deep Survival Models Can Improve Long-Term Mortality Risk Estimates from Chest Radiographs

Explore how deep survival models enhance long-term mortality risk prediction using chest radiographs.

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

Deep learning has recently demonstrated the ability to predict long-term patient risk and its stratification when trained on imaging data such as chest radiographs. However, existing methods formulate estimating patient risk as a binary classification, typically ignoring or limiting the use of tempo...

09/01/2026

📘 Must Read in !

📖 Developing Personalised Learning Support for the Business Forecasting Curriculum: The Forecasting Intelligent Tutoring System

Learn how an intelligent tutoring system can provide personalized support for business forecasting education.

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

08/01/2026

📘 Must Read in !

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

Discover how LSTM-EMD models improve day-ahead electric load forecasting across multiple case studies.

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

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...