Previous Issue
Volume 7, June
 
 

Forecasting, Volume 7, Issue 3 (September 2025) – 4 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Select all
Export citation of selected articles as:
31 pages, 1127 KiB  
Article
Optimizing Credit Risk Prediction for Peer-to-Peer Lending Using Machine Learning
by Lyne Imene Souadda, Ahmed Rami Halitim, Billel Benilles, José Manuel Oliveira and Patrícia Ramos
Forecasting 2025, 7(3), 35; https://doi.org/10.3390/forecast7030035 - 29 Jun 2025
Viewed by 301
Abstract
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, Random Forest, XGBoost, [...] Read more.
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, Random Forest, XGBoost, and LightGBM, using three real-world datasets (Lending Club, Australia, Taiwan). We assess predictive accuracy (AUC, Sensitivity, Specificity, G-Mean), computational efficiency, robustness, and interpretability. LightGBM achieves the highest AUC (e.g., 70.77% on Lending Club, 93.25% on Australia, 77.85% on Taiwan), with XGBoost performing comparably. Bayesian methods (Hyperopt, Optuna) match or approach Grid Search’s accuracy while reducing runtime by up to 75.7-fold (e.g., 3.19 vs. 241.47 min for LightGBM on Lending Club). A sensitivity analysis confirms robust hyperparameter configurations, with AUC variations typically below 0.4% under ±10% perturbations. A feature importance analysis, using gain and SHAP metrics, identifies debt-to-income ratio and employment title as key default predictors, with stable rankings (Spearman correlation > 0.95, p<0.01) across tuning methods, enhancing model interpretability. Operational impact depends on data quality, scalable infrastructure, fairness audits for features like employment title, and stakeholder collaboration to ensure compliance with regulations like the EU AI Act and U.S. Equal Credit Opportunity Act. These findings advocate Bayesian HPO and ensemble models in P2P lending, offering scalable, transparent, and fair solutions for default prediction, with future research suggested to explore advanced resampling, cost-sensitive metrics, and feature interactions. Full article
(This article belongs to the Special Issue Feature Papers of Forecasting 2025)
Show Figures

Figure 1

19 pages, 630 KiB  
Article
Forecasting Outcomes Using Multi-Option, Advantage-Sensitive Thurstone-Motivated Models
by László Gyarmati, Csaba Mihálykó and Éva Orbán-Mihálykó
Forecasting 2025, 7(3), 34; https://doi.org/10.3390/forecast7030034 - 26 Jun 2025
Viewed by 313
Abstract
In this paper, multi-option probabilistic paired comparison models are presented and applied for prediction. As these models operate on the basis of probabilities, they can estimate the likelihood of future outcomes and thus predict future events. The aim of the paper is to [...] Read more.
In this paper, multi-option probabilistic paired comparison models are presented and applied for prediction. As these models operate on the basis of probabilities, they can estimate the likelihood of future outcomes and thus predict future events. The aim of the paper is to demonstrate that these models have strong predictive capabilities when the information embedded into the data is properly utilized. To this end, we incorporate the degree (e.g., large or small) of the differences between the compared objects. By refining the usual three-option model, we define a five-option model capable of leveraging information derived from the goal differences. To incorporate additional information, the model is further extended to account for potential advantages in the comparisons. As a further refinement, temporal weighting is also introduced. These models are applied to forecasting football match outcomes in the top five European leagues (Premier League, La Liga, Serie A, Bundesliga, and Ligue 1), and their predictive performance is evaluated using various metrics. Based on the most recent football seasons, this model consistently delivers better predictive metrics, on average, than those of the already strong benchmark model. The effect of a home-field advantage is statistically supported across all five leagues. The model fits are illustrated using confidence intervals, and, as an interesting insight, we also present the evolution of the team strengths for the top four English clubs during the 2023/24 season. Full article
(This article belongs to the Section Forecasting in Economics and Management)
Show Figures

Figure 1

24 pages, 3910 KiB  
Article
Machine Learning-Based Prediction of External Pressure in High-Speed Rail Tunnels: Model Optimization and Comparison
by Xiazhou She, Yongxing Jia, Rui Li, Jianlin Xu, Yonggang Yang, Weiqiang Cao, Lei Xiao and Wenhao Zhao
Forecasting 2025, 7(3), 33; https://doi.org/10.3390/forecast7030033 - 24 Jun 2025
Viewed by 258
Abstract
The pressure fluctuations generated during high-speed train passage through tunnels can compromise both the train’s structural integrity and passenger comfort, highlighting the need for the accurate prediction of external pressure wave amplitudes. To address the high computational cost of multi-condition Computational Fluid Dynamics [...] Read more.
The pressure fluctuations generated during high-speed train passage through tunnels can compromise both the train’s structural integrity and passenger comfort, highlighting the need for the accurate prediction of external pressure wave amplitudes. To address the high computational cost of multi-condition Computational Fluid Dynamics simulations, this study proposes a hybrid method combining numerical simulation and machine learning. A dataset was generated using simulations with five input features: tunnel length, train length, train speed, blockage ratio, and measurement point location. Four machine learning models—random forest, support vector regression, Extreme Gradient Boosting, and Multilayer Perceptron (MLP)—were evaluated, with the MLP model showing the highest baseline accuracy. To further improve performance, six metaheuristic algorithms were applied to optimize the MLP model, among which, the sparrow search algorithm (SSA) achieved the highest accuracy, with R2 = 0.993, MAPE = 0.052, and RMSE = 0.112. A SHapley Additive exPlanations (SHAP) analysis indicated that the train speed and the blockage ratio were the most influential features. This study provides an effective and interpretable method for pressure wave prediction in tunnel environments and demonstrates the first integration of SSA optimization into aerodynamic pressure modeling. Full article
Show Figures

Figure 1

19 pages, 2144 KiB  
Article
Sensitivity Analysis of Priors in the Bayesian Dirichlet Auto-Regressive Moving Average Model
by Harrison Katz, Liz Medina and Robert E. Weiss
Forecasting 2025, 7(3), 32; https://doi.org/10.3390/forecast7030032 - 20 Jun 2025
Viewed by 434
Abstract
We examine how prior specification affects the Bayesian Dirichlet Auto-Regressive Moving Average (B-DARMA) model for compositional time series. Through three simulation scenarios—correct specification, overfitting, and underfitting—we compare five priors: informative, horseshoe, Laplace, mixture of normals, and hierarchical. Under correct model specification, all priors [...] Read more.
We examine how prior specification affects the Bayesian Dirichlet Auto-Regressive Moving Average (B-DARMA) model for compositional time series. Through three simulation scenarios—correct specification, overfitting, and underfitting—we compare five priors: informative, horseshoe, Laplace, mixture of normals, and hierarchical. Under correct model specification, all priors perform similarly, although the horseshoe and hierarchical priors produce slightly lower bias. When the model overfits, strong shrinkage—particularly from the horseshoe prior—proves advantageous. However, none of the priors can compensate for model misspecification if key VAR/VMA terms are omitted. We apply B-DARMA to daily S&P 500 sector trading data, using a large-lag model to demonstrate overparameterization risks. Shrinkage priors effectively mitigate spurious complexity, whereas weakly informative priors inflate errors in volatile sectors. These findings highlight the critical role of carefully selecting priors and managing model complexity in compositional time-series analysis, particularly in high-dimensional settings. Full article
(This article belongs to the Section Forecasting in Economics and Management)
Show Figures

Figure 1

Previous Issue
Back to TopTop