Journal of Forecasting最新文献_第10页

Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation 基于因果机器学习的信用风险预测：贝叶斯网络学习、违约推断和解释

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-02-27 DOI: 10.1002/for.3080

Jiaming Liu, Xuemei Zhang, Haitao Xiong

{"title":"Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation","authors":"Jiaming Liu, Xuemei Zhang, Haitao Xiong","doi":"10.1002/for.3080","DOIUrl":"10.1002/for.3080","url":null,"abstract":"The predictive and interpretable power of models is crucial for financial risk management. The purpose of this study was to perform credit risk prediction in a structured causal network with four stages—data processing, structural learning, parameter learning, and interpretation of inferences—and use six real credit datasets to conduct empirical research on the proposed model. Compared with traditional machine learning algorithms, we comprehensively explain the results of credit default through forward and reverse reasoning. We also compared our model with the post hoc interpretation models local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) to verify the interpretability of Bayesian networks. The experimental results show that the prediction performance of Bayesian networks is superior to traditional machine learning models and similar to the performance of ensemble models. Furthermore, Bayesian networks offer valuable insights into the interplay of features by considering their causal relationships and enable an assessment of how individual features influence the prediction outcome. In this study, what-if analysis was performed to assess credit default probabilities under various conditions. This analysis provides decision-makers with the necessary tools to make informed judgments about the risk profile of borrowers. Consequently, we consider Bayesian networks as a viable tool for credit risk prediction models in terms of prediction performance and interpretability.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140025863","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}

引用次数: 0

A forecasting model for oil prices using a large set of economic indicators 利用大量经济指标的石油价格预测模型

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-02-26 DOI: 10.1002/for.3087

Jihad El Hokayem, Ibrahim Jamali, Ale Hejase

引用次数: 0

Interpretable corn future price forecasting with multivariate time series 利用多变量时间序列预测可解读的玉米未来价格

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-02-25 DOI: 10.1002/for.3099

Binrong Wu, Zhongrui Wang, Lin Wang

{"title":"Interpretable corn future price forecasting with multivariate time series","authors":"Binrong Wu, Zhongrui Wang, Lin Wang","doi":"10.1002/for.3099","DOIUrl":"10.1002/for.3099","url":null,"abstract":"Efforts in corn future price forecasting and early warning play a vital role in guiding the high-quality development of the agricultural economy. However, recent years have witnessed significant fluctuations in global corn future prices due to the impact of COVID-19 and the escalating risks associated with geopolitical conflicts. Therefore, there is an urgent need for accurate and efficient methods to forecast corn future prices. To address this challenge, a novel and comprehensive framework for explainable corn future price forecasting is designed. This framework takes into account multiple factors contributing to corn price volatility, including supply and demand dynamics, policy adjustments, international market shocks, global geopolitical risks, and investor concerns within the corn market. During the data processing stage, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is utilized to thoroughly explore the volatility characteristics of historical corn future prices. Additionally, a convolutional neural network (CNN) is employed to extract essential forecasting information from corn news data. To enhance interpretability, a novel JADE–TFT interpretable corn future price prediction model is proposed. This model combines adaptive differential evolution with optional external archiving (JADE) to intelligently and efficiently optimize the parameters of the temporal fusion transformers (TFTs). Furthermore, in the empirical study, the introduction of a global geopolitical risk coefficient, Baidu indices such as “corn” and “corn price,” and quantized corn news text features is shown to improve the accuracy of corn future price predictions. The proposed corn future price prediction framework contributes to the healthy development of the global grain futures market, thereby fostering the growth and well-being of enterprises involved in the grain industry.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140016765","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}

引用次数: 0

Applying k-nearest neighbors to time series forecasting: Two new approaches 将 K 最近邻法应用于时间序列预测：两种新方法

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-02-25 DOI: 10.1002/for.3093

Samya Tajmouati, Bouazza E. L. Wahbi, Adel Bedoui, Abdallah Abarda, Mohamed Dakkon

{"title":"Applying k-nearest neighbors to time series forecasting: Two new approaches","authors":"Samya Tajmouati, Bouazza E. L. Wahbi, Adel Bedoui, Abdallah Abarda, Mohamed Dakkon","doi":"10.1002/for.3093","DOIUrl":"10.1002/for.3093","url":null,"abstract":"The k-nearest neighbors algorithm is one of the prominent techniques used in classification and regression. Despite its simplicity, the k-nearest neighbors has been successfully applied in time series forecasting. However, the selection of the number of neighbors and feature selection is a daunting task. In this paper, we introduce two methodologies for forecasting time series that we refer to as Classical Parameters Tuning in Weighted Nearest Neighbors and Fast Parameters Tuning in Weighted Nearest Neighbors. The first approach uses classical parameters tuning that compares the most recent subsequence with every possible subsequence from the past of the same length. The second approach reduces the neighbors' search set, which leads to significantly reduced grid size and hence a lower computational time. To tune the models' parameters, both methods implement an approach inspired by cross-validation for weighted nearest neighbors. We evaluate the forecasting performance and accuracy of our models. Then, we compare them to other approaches, especially, Seasonal Autoregressive Integrated Moving Average, Holt Winters, and Exponential Smoothing State Space Model. Real data examples on retail and food services sales in the United States and milk production in the United Kingdom are analyzed to demonstrate the application and efficiency of the proposed approaches.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140016711","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}

引用次数: 0

Do search queries predict violence against women? A forecasting model based on Google Trends 搜索查询能预测暴力侵害妇女行为吗？基于谷歌趋势的预测模型

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-02-25 DOI: 10.1002/for.3102

Nicolás Gonzálvez-Gallego, María Concepción Pérez-Cárceles, Laura Nieto-Torrejón

引用次数: 0

Forecasting stock market returns with a lottery index: Evidence from China 用彩票指数预测股市收益：来自中国的证据

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-02-25 DOI: 10.1002/for.3100

Yaojie Zhang, Qingxiang Han, Mengxi He

引用次数: 0

Robust approach to earnings forecast: A comparison 稳健的盈利预测方法：比较

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-02-21 DOI: 10.1002/for.3085

Xiaojian Yu, Xiaoqian Zhang, Donald Lien

引用次数: 0

Tail risk forecasting and its application to margin requirements in the commodity futures market 尾部风险预测及其在商品期货市场保证金要求中的应用

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-02-20 DOI: 10.1002/for.3094

Yun Feng, Weijie Hou, Yuping Song

引用次数: 0

Tail risk forecasting with semiparametric regression models by incorporating overnight information 通过纳入隔夜信息，利用半参数回归模型预测尾端风险

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-02-20 DOI: 10.1002/for.3090

Cathy W. S. Chen, Takaaki Koike, Wei-Hsuan Shau

引用次数: 0

Probabilistic electricity price forecasting based on penalized temporal fusion transformer 基于惩罚性时态融合变压器的概率电价预测

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-02-20 DOI: 10.1002/for.3084

He Jiang, Sheng Pan, Yao Dong, Jianzhou Wang

{"title":"Probabilistic electricity price forecasting based on penalized temporal fusion transformer","authors":"He Jiang, Sheng Pan, Yao Dong, Jianzhou Wang","doi":"10.1002/for.3084","DOIUrl":"https://doi.org/10.1002/for.3084","url":null,"abstract":"<div>\u0000 \u0000 In the deregulated electricity market, it is increasingly important to accurately predict the fluctuating, nonlinear, and high-frequent electricity price for market decision-making. However, the uncertainties associated with electricity prices, such as non-stationarity, nonlinearity, and high volatility, pose critical difficulties for electricity price forecasting (EPF). Unlike point forecasting, which provides only a single, deterministic estimate of future prices, probabilistic forecasting gives a more comprehensive and nuanced picture of future price dynamics, which can help market participants make better-informed decisions when facing uncertainty. Therefore, in this paper, we propose a robust deep learning method for multi-step probabilistic forecasting. First, we use the least absolute shrinkage and selection operator (LASSO) in the expert model to generate point forecasts. Second, we introduce the smoothly clipped absolute deviation regularization term, a nonconvex penalty with proven oracle properties in model selection, into temporal fusion transformers. Finally, we employ the proposed model to integrate point forecasts to give probabilistic forecasts. To evaluate the proposed forecasting model, real-data experiments are conducted in the Nord Pool electricity market and the Polish Power Exchange market. Empirical results show that the proposed model has demonstrated superior probabilistic forecasting performances compared with other competitors and has proven its effectiveness in real-world applications.\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141536922","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}

引用次数: 0