Forecasting

16 pages, 1383 KiB

Open AccessArticle

Probabilistic Demand Forecasting in the Southeast Region of the Mexican Power System Using Machine Learning Methods

by Ivan Itai Bernal Lara, Roberto Jair Lorenzo Diaz, María de los Ángeles Sánchez Galván, Jaime Robles García, Mohamed Badaoui, David Romero Romero and Rodolfo Alfonso Moreno Flores

Forecasting 2025, 7(3), 39; https://doi.org/10.3390/forecast7030039 - 18 Jul 2025

Viewed by 192

Abstract

This paper focuses on electricity demand forecasting and its uncertainty representation using a hybrid machine learning (ML) model in the eastern control area of southeastern Mexico. In this case, different sources of uncertainty are integrated by applying the Bootstrap method, which adds the [...] Read more.

This paper focuses on electricity demand forecasting and its uncertainty representation using a hybrid machine learning (ML) model in the eastern control area of southeastern Mexico. In this case, different sources of uncertainty are integrated by applying the Bootstrap method, which adds the characteristics of stochastic noise, resulting in a hybrid probabilistic and ML model in the form of a time series. The proposed methodology addresses a function density probability, which is the generalized of extreme values obtained from the errors of the ML model; however, it is adaptable and independent and simulates the variability that may arise due to unforeseen events. Results indicate that for a five-day forecast using only demand data, the proposed model achieves a Mean Absolute Percentage Error (MAPE) of 4.358%; however, incorporating temperature increases the MAPE to 5.123% due to growing uncertainty. In contrast, a day-ahead forecast, including temperature, improves accuracy, reducing MAPE to 1.644%. The stochastic noise component enhances probabilistic modeling, yielding a MAPE of 3.042% with and 2.073% without temperature in five-day forecasts. Therefore, the proposed model proves useful for regions with high demand variability, such as southeastern Mexico, while maintaining accuracy over longer time horizons. Full article

(This article belongs to the Section Power and Energy Forecasting)

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17 pages, 434 KiB

Open AccessArticle

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

by Albin Uruqi and Iosif Viktoratos

Forecasting 2025, 7(3), 38; https://doi.org/10.3390/forecast7030038 - 18 Jul 2025

Viewed by 224

Abstract

This study explores the application of spiking neural networks (SNNs) for click-through rate (CTR) prediction in personalized online advertising systems, introducing a novel hybrid model, the Temporal Rate Spike with Attention Neural Network (TRA–SNN). By leveraging the biological plausibility and energy efficiency of [...] Read more.

This study explores the application of spiking neural networks (SNNs) for click-through rate (CTR) prediction in personalized online advertising systems, introducing a novel hybrid model, the Temporal Rate Spike with Attention Neural Network (TRA–SNN). By leveraging the biological plausibility and energy efficiency of SNNs, combined with attention-based mechanisms, the TRA–SNN model captures temporal dynamics and rate-based patterns to achieve performance comparable to state-of-the-art Artificial Neural Network (ANN)-based models, such as Deep & Cross Network v2 (DCN-V2) and FinalMLP. The models were trained and evaluated on the Avazu and Digix datasets, using standard metrics like AUC-ROC and accuracy. Through rigorous hyperparameter tuning and standardized preprocessing, this study ensures fair comparisons across models, highlighting SNNs’ potential for scalable, sustainable deployment in resource-constrained environments like mobile devices and large-scale ad platforms. This work is the first to apply SNNs to CTR prediction, setting a new benchmark for energy-efficient predictive modeling and opening avenues for future research in hybrid SNN–ANN architectures across domains like finance, healthcare, and autonomous systems. Full article

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20 pages, 557 KiB

Open AccessArticle

Forecasting Youth Unemployment Through Educational and Demographic Indicators: A Panel Time-Series Approach

by Arsen Tleppayev and Saule Zeinolla

Forecasting 2025, 7(3), 37; https://doi.org/10.3390/forecast7030037 - 16 Jul 2025

Viewed by 154

Abstract

Youth unemployment remains a pressing issue in many emerging economies, where educational disparities and demographic pressures interact in complex ways. This study investigates the links between higher-education enrolment, demographic structure and youth unemployment in eight developing countries from 2009 to 2023. Panel cointegration [...] Read more.

Youth unemployment remains a pressing issue in many emerging economies, where educational disparities and demographic pressures interact in complex ways. This study investigates the links between higher-education enrolment, demographic structure and youth unemployment in eight developing countries from 2009 to 2023. Panel cointegration techniques—Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS)—are applied to estimate the long-run effects of gross tertiary-school enrolment on youth unemployment while controlling for GDP growth and youth-cohort size. Robustness is confirmed through complementary estimations with pooled-mean-group ARDL and system-GMM panels, which deliver consistent coefficient signs and significance levels. Results show a significant negative elasticity between enrolment and youth unemployment, indicating that wider access to higher education helps lower joblessness among young people. Youth-population growth exerts an opposite, positive effect, while GDP growth reduces unemployment but less uniformly across regions. The evidence points to an integrated policy mix—expanding tertiary (especially vocational and technical) education, managing demographic pressure and maintaining macro-economic stability—to improve youth-employment outcomes in emerging economies. Full article

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49 pages, 1398 KiB

Open AccessReview

Navigating AI-Driven Financial Forecasting: A Systematic Review of Current Status and Critical Research Gaps

by László Vancsura, Tibor Tatay and Tibor Bareith

Forecasting 2025, 7(3), 36; https://doi.org/10.3390/forecast7030036 - 14 Jul 2025

Viewed by 765

Abstract

This systematic literature review explores the application of artificial intelligence (AI) and machine learning (ML) in financial market forecasting, with a focus on four asset classes: equities, cryptocurrencies, commodities, and foreign exchange markets. Guided by the PRISMA methodology, the study identifies the most [...] Read more.

This systematic literature review explores the application of artificial intelligence (AI) and machine learning (ML) in financial market forecasting, with a focus on four asset classes: equities, cryptocurrencies, commodities, and foreign exchange markets. Guided by the PRISMA methodology, the study identifies the most widely used predictive models, particularly LSTM, GRU, XGBoost, and hybrid deep learning architectures, as well as key evaluation metrics, such as RMSE and MAPE. The findings confirm that AI-based approaches, especially neural networks, outperform traditional statistical methods in capturing non-linear and high-dimensional dynamics. However, the analysis also reveals several critical research gaps. Most notably, current models are rarely embedded into real or simulated trading strategies, limiting their practical applicability. Furthermore, the sensitivity of widely used metrics like MAPE to volatility remains underexplored, particularly in highly unstable environments such as crypto markets. Temporal robustness is also a concern, as many studies fail to validate their models across different market regimes. While data covering one to ten years is most common, few studies assess performance stability over time. By highlighting these limitations, this review not only synthesizes the current state of the art but also outlines essential directions for future research. Specifically, it calls for greater emphasis on model interpretability, strategy-level evaluation, and volatility-aware validation frameworks, thereby contributing to the advancement of AI’s real-world utility in financial forecasting. Full article

(This article belongs to the Section Forecasting in Computer Science)

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31 pages, 1127 KiB

Open AccessArticle

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 418

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

\pm 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)

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19 pages, 630 KiB

Open AccessArticle

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 332

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)

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24 pages, 3910 KiB

Open AccessArticle

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 294

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

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19 pages, 2144 KiB

Open AccessArticle

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 491

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)

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Forecasting, Volume 7, Issue 3 (September 2025) – 8 articles

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