Journal of Forecasting最新文献

{"title":"Forecasting Volatility in the Chinese Stock Market Using Deep Learning-Based Hybrid Factor Models","authors":"Nan Hu,&nbsp;Fengping Tian","doi":"10.1002/for.70192","DOIUrl":"https://doi.org/10.1002/for.70192","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper develops deep learning-based hybrid factor models for forecasting firm-specific volatility in the Chinese stock market. The proposed models integrate deep learning-based instrumented principal component analysis (IPCA) with an autoregressive module, allowing a rich set of observable features to inform the extraction of forecasting-relevant common factors and factor loadings while accounting for the persistent dynamics of volatility. The empirical results show that the hybrid models can effectively forecast firm-specific volatility over the full out-of-sample period and during extreme market episodes. Furthermore, we conduct a detailed investigation into the marginal contributions of different feature groups over time. The results indicate that factors such as price trends, sentiment measures, money and credit indicators, stock market variables, and cumulative news sentiment significantly improve the accuracy of volatility forecasts.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 7","pages":"3381-3397"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Non-Parametric tv-GARCH Model With a Space-State Representation for Forecasting","authors":"Guillermo Ferreira,&nbsp;Jorge A. Muñoz-Mendoza,&nbsp;Jorge Arratia-Llancao,&nbsp;Jorge Mateu,&nbsp;Miguel Flores-Sánchez,&nbsp;Francisco J. Rodríguez-Cortés","doi":"10.1002/for.70188","DOIUrl":"https://doi.org/10.1002/for.70188","url":null,"abstract":"<div>\u0000 \u0000 <p>We propose a state-space approach for a non-parametric tv-GARCH model. Using a number of non-parametric techniques, we estimate slowly time-varying curves for the parameters and combine it with a recursive system based on the Kalman filter as a methodological framework to obtain forecasts from the model. Our results based on Monte Carlo simulations and an application to the Selective Stock Price Index of the Chilean stock market indicate that the non-parametric tv-GARCH model shows the best fit and provides the best forecasting performance, compared with the stationary GARCH model and other techniques, such as tv-GARCH with time-varying parameters determined by deterministic functions. These results have relevant implications for risk management, portfolio diversification, and asset allocation.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 7","pages":"3342-3357"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantum-Inspired Chimp Optimizer Evolving Kernel Extreme Learning Machine for Financial Risk Prediction: A Case Study on Bankruptcy","authors":"Yuchun Li,&nbsp;Mohammad Khishe,&nbsp;Halmat Ayub,&nbsp;Aseel Smerat","doi":"10.1002/for.70171","DOIUrl":"https://doi.org/10.1002/for.70171","url":null,"abstract":"<div>\u0000 \u0000 <p>Kernel-based extreme learning machines (KBELMs) have demonstrated great potential in predicting bankruptcy because of their rapid learning capability and the ability to model nonlinear financial relationships. However, the predictive performance of KBELM is very sensitive to the choice of hyperparameters, and the traditional tuning strategies tend to have the shortcomings of early convergence and insufficient search space exploration. To overcome these problems, this study proposes a quantum-inspired chimp optimization algorithm (QICHOA) for effective hyperparameter optimization of KBELM, and the resultant hybrid model is called KBELM-QICHOA. The proposed optimizer is an improvement of the classical chimp optimization algorithm by adding quantum-inspired mechanisms for better global search capability and balancing exploration and exploitation. The performance of KBELM-QICHOA is evaluated using two real-world bankruptcy datasets, that is, Wieslaw dataset and Japanese bankruptcy dataset, under a nested cross-validation framework. The proposed model is compared with five benchmark approaches, which are conventional KBELM, KBELM-HFDO, KBELM-HAOA, KBELM-RCGWO, and KBELM-IPBBO. Experimental results show that KBELM-QICHOA is significantly better than competing models in terms of prediction accuracy, robustness and stability in terms of RMSE, Nash–Sutcliffe efficiency (NSEF), and bias. The results show that combining quantum-inspired optimization with kernel-based learning has a significant impact on improving the performance of bankruptcy prediction. The proposed KBELM-QICHOA framework therefore constitutes a reliable and economically meaningful early warning tool for the financial risk assessment and decision support.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 7","pages":"3079-3100"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Computation of Lead–Lag Signature Features for Financial Time-Series Forecasting","authors":"Insu Choi","doi":"10.1002/for.70176","DOIUrl":"https://doi.org/10.1002/for.70176","url":null,"abstract":"<div>\u0000 \u0000 <p>We develop an efficient algorithm for computing lead–lag signature features derived from rough path theory and evaluate its application to financial return prediction. The path signature provides a universal nonlinear feature map for sequential data, but naïve computation is intractable as truncation depth grows. We exploit the algebraic structure of Chen's identity to derive incremental updates for depth-2 signatures, achieving substantially lower computational complexity, and prove that the resulting algorithm is numerically stable. The lead–lag transformation embeds univariate series into a two-dimensional path, recovering realized quadratic variation as the Lévy area—a quantity lost in one-dimensional signatures. In an empirical evaluation on a panel of financial assets spanning equities, fixed income, currencies, commodities, and cryptocurrency, signature features yield their largest gains over technical indicators on long-duration Treasuries, where bond convexity creates path-dependent return dynamics; they offer little incremental advantage on equities, where simple momentum features perform best. We further document a divergence between statistical predictability and economic profitability: a signature-based gold strategy attains a high backtested Sharpe ratio despite a negative unconditional correlation between forecasts and realized returns.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 7","pages":"3263-3277"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graphical Regularized Quantile Regression With Reinforcement Learning","authors":"He Jiang,&nbsp;Qiang Liu","doi":"10.1002/for.70181","DOIUrl":"https://doi.org/10.1002/for.70181","url":null,"abstract":"<div>\u0000 \u0000 <p>Time series forecasting is a fundamental task in scientific and engineering disciplines. Quantile regression (QR) has gained substantial popularity due to its flexibility in modeling conditional distributions without stringent parametric assumptions. However, traditional QR approaches often overlook the geometric dependencies and structural relationships among predictors, leading to suboptimal performance in complex forecasting scenarios. To address this gap, this paper proposes a novel framework that integrates graphical regularization with sparse quantile regression, enhanced by an \u0000<span></span><math>\u0000 <mi>ϵ</mi></math>-greedy reinforcement learning (RL) strategy for efficient parameter tuning. Our model incorporates a graph Laplacian matrix to preserve spatial structures among predictors while maintaining the robustness of QR. The resulting optimization problem is solved efficiently using the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm. Empirical studies on real-world electricity market datasets (Belgian and American markets) demonstrate that our approach, designated as RL-RG-SCAD(0.5), significantly outperforms state-of-the-art statistical and deep learning models. These improvements are rigorously validated by Diebold-Mariano tests, Giacomini-White tests, and Hansen's superior predictive ability test. The framework offers enhanced accuracy and interpretability by effectively leveraging predictor structures.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 7","pages":"3191-3203"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting EU Emissions Allowance Prices Using Macroeconomic Indicators and Hybrid AI Models","authors":"Saptarshi Ganguly,&nbsp;Pooja Sengupta,&nbsp;Divya Aggarwal","doi":"10.1002/for.70173","DOIUrl":"https://doi.org/10.1002/for.70173","url":null,"abstract":"<p>Predicting carbon allowance prices has grown more crucial in relation to carbon market regulation, financial strategy, and environmental policy development. This study examines a hybrid forecasting system that combines deep learning with ensemble machine learning models to forecast the price fluctuations of EU Emissions Allowance (EUAs) within the European Union Emissions Trading System (EU ETS). By leveraging a dataset that includes past EUA prices alongside macroeconomic factors like exchange rates, stock indices, natural gas, and crude oil prices, we evaluate the forecasting capabilities of long short-term memory (LSTM) neural networks, random forest (RF), and extreme gradient boosting (XGBoost) models. These models are assessed using commonly recognized metrics: root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The findings suggest that LightGBM and XGBoost outshine Random Forest and LSTM in performance, with XGBoost emerging as the best predictor model. XGBoost specializes in capturing complex connections within structured data by managing nonlinear connections and interactions. Considering that carbon markets operate in phases, with each phase bringing its own set of reforms, this study offers novel results. In an integrated market prediction of evolving asset class series, LSTM models that are considered more adept at handling sequential data like time series might not yield superior forecasting performance. Our results emphasize the capability of ensemble learning approach forecasting systems to improve prediction accuracy in emissions trading markets, providing important information for policymakers, financial analysts, and sustainability strategists. The hybrid method discussed here demonstrates how AI-based analytics can enhance more resilient and data-informed environmental decision-making.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 7","pages":"3170-3190"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.70173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"THD-Net: Temporal Heterogeneity Decoupling for Traffic Flow Forecasting","authors":"Ru Xue,&nbsp;Yuan Xue,&nbsp;Xiang Wang,&nbsp;Di Wu,&nbsp;Zirong Wang,&nbsp;Yucong Zhang","doi":"10.1002/for.70190","DOIUrl":"https://doi.org/10.1002/for.70190","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate traffic-flow forecasting underpins route guidance, signal control, and proactive congestion management, but fine-resolution traffic series are not governed by a single temporal pattern. Abrupt local disturbances, slower congestion evolution, and recurrent mobility routines contribute differently as the forecast horizon increases, making a homogeneous temporal representation inadequate for direct multihorizon prediction. This study proposes THD-Net, a temporal-heterogeneity decoupling network that assigns distinct modeling roles before fusion. A DHA local-response branch captures recent and weekly lag-informed dynamics, whereas a subseries Transformer branch abstracts long-memory interactions and supports trend- and recurrence-oriented summaries; a frequency-inspired horizon-profile regularizer constrains the shape of the four-horizon output. On urban and freeway PeMS scenarios, THD-Net shows a horizon-dependent advantage rather than uniform short-horizon dominance. Strong baselines remain competitive at 5 and 15 min, whereas THD-Net achieves the lowest five-seed mean MAE among the compared deep baselines at both 30 and 60 min on both datasets. Paired day-by-seed Wilcoxon tests further support the stability of these medium- and long-horizon gains. These results indicate that explicit temporal-role specialization is a useful inductive bias for robust traffic-flow forecasting when local fluctuations and slower temporal regularities must be predicted jointly.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 7","pages":"3368-3380"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized Internet of Things-Based Intelligent Energy Management System for Enhanced Renewable Generation through Advanced Forecasting and Load Strategies","authors":"X. Mercilin Raajini,&nbsp;G. Rajesh","doi":"10.1002/for.70157","DOIUrl":"https://doi.org/10.1002/for.70157","url":null,"abstract":"<div>\u0000 \u0000 <p>An Internet of Things (IoT)-based intelligent energy management system enables monitoring and control of renewable energy generation. It lowers waste and increases energy efficiency via the use of sophisticated forecasting and load optimization techniques. However, challenges such as data inconsistency processing and integration of diverse energy sources still need to be addressed. In this paper, an optimized IoT-based intelligent energy management system for enhanced renewable generation through advanced forecasting and load strategies (IOT-IEMS-RGFLS-GPTPINN) is proposed. Firstly, the input data is gathered from the National Solar Radiation Database. Then the input data is preprocessed using an adaptive higher-order singular value decomposition clutter filter (AHOSVDC) for normalization. The preprocessed data is then fed into the prediction segment by using generative pretrained physics-informed neural networks (GPT-PINN) to predict the accurately forecast renewable energy generation and load demand. The battlefield optimization algorithm (BFOA) is used for enhancing GPT-PINN parameters. The proposed IOT-IEMS-RGFLS-GPTPINN technique is executed in Python. The proposed method's performance was evaluated using performance indicators such as mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The IOT-IEMS-RGFLS-GPTPINN achieves an MAE of 0.011%, outperforming the other existing methods SAT-IRES-IOT for 0.052%, RA-DSEM-PSO for 0.034%, and EDSM-SIMS-LEO for 0.063%. This includes existing methods such as Enhanced demand-side management for solar-based isolated microgrid systems: Load prioritization and energy optimization (EDSM-SIMS-LEO), Recent advancement in demand-side energy management systems for optimal energy utilization (RA-DSEM-PSO), and Smart agriculture technology: An integrated framework of renewable energy resources, IoT-based energy management, and precision robotics (SAT-IRES-IOT).</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 7","pages":"3219-3231"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting COVID-19 Transmission: Insights From the Logistic Smooth Transition Model","authors":"Yeliz Uymaz,&nbsp;Tolga Omay","doi":"10.1002/for.70182","DOIUrl":"https://doi.org/10.1002/for.70182","url":null,"abstract":"<div>\u0000 \u0000 <p>This study aims to explain the transmission mechanism of the COVID-19 pandemic through short-term forecasts based on the logistic smooth transition (LST) model. The model simulation was applied to data from the first seven waves in Japan, as well as the first waves of the other G7 countries. The model provided forecasts with significant precision, with an average of 59 days ahead of the actual number of COVID-19 cases. The model effectively estimated transition speed and identified critical thresholds for each pandemic wave, facilitating early predictions of peak timings. This framework not only enhances our understanding of COVID-19 transmission dynamics but also offers a replicable method for managing future pandemics.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 7","pages":"3204-3218"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting Quarterly GDP Growth Rates Using Machine Learning Methods","authors":"Kristian Jönsson","doi":"10.1002/for.70184","DOIUrl":"https://doi.org/10.1002/for.70184","url":null,"abstract":"<div>\u0000 \u0000 <p>Predicting current and near-term macroeconomic developments using linear indicator models and factor models, together with Economic Tendency Survey data, is standard nowcasting practice. In the current article, it is investigated whether machine learning (ML) methods, when used together with a limited set of tendency survey confidence indicators, can improve the forecasts of Swedish quarterly GDP growth compared with linear indicator models and factor models. The results indicate that ML methods generally perform relatively well. In particular, gradient-boosted regression trees, random forests, and multilayer perceptron models are identified as some of the best performing models. The results indicate that ML models can be fruitfully applied in macroeconomic forecasting without employing vast amounts of data. One factor contributing to this could be the ability of ML methods to capture nonlinearities. Results also indicate that, when implementing ML models, care should be taken in determining how often central model parameters should be tuned and estimated.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 7","pages":"3232-3262"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}