Journal of Forecasting最新文献

{"title":"Research on occupant injury severity prediction of autonomous vehicles based on transfer learning","authors":"Na Yang, Dongwei Liu, Qi Liu, Zhiwei Li, Tao Liu, Jianfeng Wang, Ze Xu","doi":"10.1002/for.3186","DOIUrl":"https://doi.org/10.1002/for.3186","url":null,"abstract":"The focus of the future of autonomous vehicles has shifted from feasibility to safety and comfort. The seat of an autonomous vehicle may be equipped with a rotational function, and the occupant's sitting position would be diverse. This poses a higher challenge to occupant injury protection during vehicle collisions. The main objective of the current study is to develop occupant injury prediction models for autonomous vehicles that can be used to predict the injury severity of occupants in different seat orientations and sitting positions. The first step is to establish an occupant crash model database with different seat orientations. It is used to simulate the occupant crash injury database of an autonomous vehicle, considering seat rotation and the back inclination angle. The second step is to establish a pre‐training occupant injury prediction model based on the existing database and then train the autonomous vehicle occupant injury prediction model using an in‐house database based on the transfer learning method. Occupant injury prediction models achieve good accuracy (82.8% on the numerical database and 62.9% on the real verification database) and shorter computational time (4.86 ± 0.33 ms) on the prediction tasks. Finally, the influence of the model input variables is analyzed. This study demonstrates the feasibility of using a small‐sample database based on transfer learning for occupant injury prediction in autonomous vehicles.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"30 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141936356","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":"Short‐term multivariate airworthiness forecasting based on decomposition and deep prediction models","authors":"Ali Tatli, Tansu Filik, Erdogan Bocu, Hikmet Tahir Karakoc","doi":"10.1002/for.3179","DOIUrl":"https://doi.org/10.1002/for.3179","url":null,"abstract":"This study introduces a model for predicting airworthiness in terms of meteorology information within the viewpoint of not only formal regulations but also informal rules based on acquired indicators from flight training organization experience (AIs‐FTOE). The case study is carried out in the Hasan Polatkan Airport which is used by the Department of Flight Training of Eskişehir Technical University (ESTU‐P), which is also recognized as a flight training organization. Within the study, the constraints (derived from regulations and AIs‐FTOE) and the data set used in models are explained. Also, the models are introduced based on the gated recurrent unit (GRU) and long short‐term memory (LSTM) with the use of empirical mode decomposition (EMD) and variational mode decomposition (VMD). Finally, a model‐selective mechanism (MSM) is proposed to use the models in common. The findings show that the models presented in the study produce successful results that can be used in flight training organization's (FTO) planning studies. The MSM uses GRU and LSTM together with decomposition techniques to provide more advanced prediction capabilities. When the literature is examined, it is observed that although meteorological conditions are of vital importance in the efficiency of FTOs, there are not enough studies on airworthiness based on meteorology. So, a model that will assist in scheduling plans is presented for FTOs. Airworthiness analysis of forecasting can provide a comprehensive reference to support planning efficiency in FTOs. To the authors' knowledge, this study will be the first in the literature on airworthiness that presents the MSM using a hybrid deep learning algorithm and decomposition of time series models in concurrent.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"132 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141936492","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":"Shapley-value-based forecast combination","authors":"Philip Hans Franses,&nbsp;Jiahui Zou,&nbsp;Wendun Wang","doi":"10.1002/for.3178","DOIUrl":"10.1002/for.3178","url":null,"abstract":"<p>This paper puts forward a new and simple method to combine forecasts, which is particularly useful when the forecasts are strongly correlated. It is based on the Mincer Zarnowitz regression, and a subsequent determination using Shapley values of the weights of the forecasts in a new combination. For a stylized case, it is proved that such a Shapley-value-based combination improves upon an equal-weight combination. Simulation experiments and a detailed illustration show the merits of the Shapley-value-based forecast combination.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 8","pages":"3194-3202"},"PeriodicalIF":3.4,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3178","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141936357","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":"New forecasting methods for an old problem: Predicting 147 years of systemic financial crises","authors":"Emile du Plessis, Ulrich Fritsche","doi":"10.1002/for.3184","DOIUrl":"https://doi.org/10.1002/for.3184","url":null,"abstract":"This paper develops new forecasting methods for an old and ongoing problem by employing 13 machine learning algorithms to study 147 years of systemic financial crises across 17 countries. Findings suggest that fixed capital formation is the most important variable. GDP per capita and consumer inflation have increased in prominence whereas debt‐to‐GDP, stock market, and consumption were dominant at the turn of the 20th century. A lag structure and rolling window both improve on optimized contemporaneous and individual country formats. Through a lag structure, banking sector predictors on average describe 28% of the variation in crisis prevalence, the real sector 64%, and the external sector 8%. Nearly half of all algorithms reach peak performance through a lag structure. As measured through AUC, and Brier scores, top‐performing machine learning methods consistently produce high accuracy rates, with both random forests and gradient boosting in front with 77% correct forecasts, and consistently outperform traditional regression algorithms. Learning from other countries improves predictive strength, and non‐linear models generally deliver higher accuracy rates than linear models. Algorithms retaining all variables perform better than those minimizing the influence of variables.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"16 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141936493","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 GARCH model selection and estimation method based on neural network with the loss function of mean square error and model confidence set","authors":"Yanhao Huang,&nbsp;Ruibin Ren","doi":"10.1002/for.3175","DOIUrl":"10.1002/for.3175","url":null,"abstract":"<p>This paper proposes a method that uses mean square error (MSE) and model confidence set (MCS) as the loss function of back-propagation neural network (BPNN), aiming to train and find a generalized autoregressive conditional heteroskedastic (GARCH) model that has the best forecasting performance of a time series. Combining MSE and the <i>p</i>-value of MCS can not only estimate better parameters for the GARCH models but also find the best GARCH model to forecast the volatility of a time series. Meanwhile, we divide a time series into several parts and use each part as the input of the BPNN. Through the BPNN, each part of the time series will be turned into several forecasting values. These values will be used to calculate the MSE and the <i>p</i>-value of MCS, which will then be used to update the parameters of the BPNN. In the end, we use MCS to choose the best GARCH model among the trained GARCH models and compare this method with maximum likelihood estimation (MLE) and the generalized least squares estimation (GLS). The result shows that the <i>p</i>-value of MCS of the best model estimated by this method is higher than the <i>p</i>-value of MCS of the best model estimated by MLE and GLS. According to the theory of MCS, a model that has a larger <i>p</i>-value does have a better forecasting performance. The method proposed by this paper can provide a potential application of neural network in GARCH model forecasting and estimation.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 8","pages":"3177-3193"},"PeriodicalIF":3.4,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141936494","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":"Design of a precise ensemble expert system for crop yield prediction using machine learning analytics","authors":"Deeksha Tripathi,&nbsp;Saroj K. Biswas","doi":"10.1002/for.3183","DOIUrl":"10.1002/for.3183","url":null,"abstract":"<p>Agriculture is facing significant challenges in the development of crop yield forecasts, which are important aspects of decision-making at the international, regional, and local levels. The area of agriculture is attracting growing attention because of increasing the demand for food supplies. To ensure future food supplies, crop yield prediction (CYP) provides the best decision-making to assist farmers in agricultural yield forecasting efficiently. Nevertheless, CYP is a difficult endeavor because of the intricacy of the underlying mechanisms and the effect of numerous factors, including weather patterns, soil characteristics, and crop management techniques. In today's era, ensemble learning (EL) approaches have recently demonstrated significant promise for enhancing the reliability and accuracy of CYP. The success of the EL techniques depends on several facts, including how the base learner models are trained and how these are combined. This study provides important insights into the EL techniques for CYP. This paper proposes an expert system model named precise ensemble expert system for crop yield prediction (PEESCYP) to predict the best crop for agricultural land. The proposed PEESCYP model employs multiple imputation by chained equation (MICE) data imputation technique to treat the missing values of the collected dataset, the isolation forest (IF) technique for outlier detection, the ant colony optimization (ACO) technique to perform feature selection, robust scaling (RS) technique to perform data normalization, and the extra tree (ET) is used for classification to overcome the variance and overfitting problem of the single classifiers. The measurements of the proposed PEESCYP model have been collected by means of accuracy, precision, recall, and F-1 score using a prepared dataset, which is collected from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), and the proposed model is compared with different single-classifier based ML models, EL models, and various existing models available in the literature. The results of this experiment underline that the proposed PEESCYP model outperforms the others.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 8","pages":"3161-3176"},"PeriodicalIF":3.4,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141936495","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":"Corporate financial distress prediction in a transition economy","authors":"Minh Nguyen,&nbsp;Bang Nguyen,&nbsp;Minh-Lý Liêu","doi":"10.1002/for.3177","DOIUrl":"10.1002/for.3177","url":null,"abstract":"<p>Forecasting financial distress of corporations is a difficult task in economies undergoing transition, as data are scarce and are highly imbalanced. This research tackles these difficulties by gathering reliable financial distress data in the context of a transition economy and employing the synthetic minority oversampling technique (SMOTE). The study employs seven different models, including linear discriminant analysis (LDA), logistic regression (LR), support vector machines (SVMs), neural networks (NNs), decision trees (DTs), random forests (RFs), and the Merton model, to predict financial distress among publicly traded companies in Vietnam between 2011 and 2021. The first six models use accounting-based variables, while the Merton model utilizes market-based variables. The findings indicate that while all models perform fairly well in predicting results for nondelisted firms, they perform somewhat poorly in predicting results for delisted firms in terms of various measures including balanced accuracy, Matthews correlation coefficient (MCC), precision, recall, and \u0000<span></span><math>\u0000 <msub>\u0000 <mrow>\u0000 <mi>F</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 </msub></math> score. The study shows that the models that incorporate both Altman's and Ohlson's variables consistently outperform those that only use Altman's or Ohlson's variables in terms of balanced accuracy. Additionally, the study finds that NNs are generally the most effective models in terms of both balanced accuracy and MCC. The most important variable in Altman's variables as well as the combination of Altman's and Ohlson's variables is “reat” (retained earnings on total assets), whereas “ltat” (total liabilities on total assets) and “wcapat” (working capital on total assets) are the most important variables in Ohlson's variables. The study also reveals that in most cases, the models perform better in predicting results for big firms than for small firms and typically better than in good years than for bad years.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 8","pages":"3128-3160"},"PeriodicalIF":3.4,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141936358","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":"High frequency volatility of oil futures in China: Components, modeling, and prediction","authors":"Yi Hong,&nbsp;Xiaofan Xu,&nbsp;Chen Yang","doi":"10.1002/for.3173","DOIUrl":"https://doi.org/10.1002/for.3173","url":null,"abstract":"<p>This paper investigates the high-frequency volatility modeling and prediction for crude oil futures in China, a new asset class emerging in recent years. Two volatility measures, the realized variance (\u0000<span></span><math>\u0000 <mi>RV</mi></math>) and realized bi-power variations (\u0000<span></span><math>\u0000 <mi>RBV</mi></math>) are constructed at various frequencies by virtue of 1-minute crude oil futures prices. The distinctive components of these volatility estimators are further identified to exploit the information contents in the in-sample explanatory power of the realized variance dynamics and the out-of-sample prediction of realized variance across different horizons, leading to four new HAR-RV-type models. First, the empirical results show that the continuous component of the weekly realized variance, representing investors' trading behavior in the medium-term, is the dominant factor driving up volatility trends in China's crude oil futures market over a range of market conditions. Second, the monthly jump component in realized variance presents the significant in-sample explanatory power, and yet marginally improves prediction performance in realized variance during the two out-of-sample periods. Finally, these results are robust toward various market/model setups, over day- and night-trading hours, and across a range of prediction horizons and relative to prediction benchmarks.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 8","pages":"3104-3127"},"PeriodicalIF":3.4,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579654","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":"Forecast performance of noncausal autoregressions and the importance of unit root pretesting","authors":"Frédérique Bec,&nbsp;Heino Bohn Nielsen","doi":"10.1002/for.3172","DOIUrl":"10.1002/for.3172","url":null,"abstract":"<p>Based on a large simulation study, this paper investigates which strategy to adopt in order to choose the most accurate forecasting model for mixed causal-noncausal autoregressions (MAR) data generating processes: always differencing (D), never differencing (L), or unit root pretesting (P). Relying on recent econometric developments regarding forecasting and unit root testing in the MAR framework, the main results suggest that from a practitioner's point of view, the P strategy at the 10% level is a good compromise. In fact, it never departs too much from the best model in terms of forecast accuracy, unlike the L (respectively, D) strategy when the DGP becomes very persistent (respectively, less persistent). This approach is illustrated using recent monthly Brent crude oil price data.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 8","pages":"3072-3088"},"PeriodicalIF":3.4,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141869804","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":"Forecasting sovereign CDS spreads with a regime-switching combination method","authors":"Jianping Li,&nbsp;Qianqian Feng,&nbsp;Jun Hao,&nbsp;Xiaolei Sun","doi":"10.1002/for.3174","DOIUrl":"10.1002/for.3174","url":null,"abstract":"<p>With the growing importance of the sovereign credit default swap (CDS) market, accurate forecasting of sovereign CDS spreads has gained significant attention. In view of the complex volatility in the series of sovereign CDS spreads, this study presents a novel combination forecasting framework, which introduces time-varying weights to effectively combine diverse individual models. To identify optimal subsets of models, a mutual information approach is employed, while the regime-switching method is utilized to integrate the selected models. The proposed method's efficacy is validated using data from 65 countries. Empirical findings underscore the superiority of the proposed approach over benchmark models in terms of both horizontal and directional prediction accuracy, particularly when the sovereign CDS data exhibits a balanced distribution between high and low volatility regimes.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 8","pages":"3089-3103"},"PeriodicalIF":3.4,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141869803","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}