Journal of Forecasting最新文献

Deep Dive Into Churn Prediction in the Banking Sector: The Challenge of Hyperparameter Selection and Imbalanced Learning 深入研究银行业的客户流失预测：超参数选择和不平衡学习的挑战

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-09-05 DOI: 10.1002/for.3194

Vasileios Gkonis, Ioannis Tsakalos

{"title":"Deep Dive Into Churn Prediction in the Banking Sector: The Challenge of Hyperparameter Selection and Imbalanced Learning","authors":"Vasileios Gkonis, Ioannis Tsakalos","doi":"10.1002/for.3194","DOIUrl":"https://doi.org/10.1002/for.3194","url":null,"abstract":"Forecasting customer churn has long been a major issue in the banking sector because the early identification of customer exit is crucial for the sustainability of banks. However, modeling customer churn is hampered by imbalanced data between classification classes, where the churn class is typically significantly smaller than the no‐churn class. In this study, we examine the performance of deep neural networks for predicting customer churn in the banking sector, while incorporating various resampling techniques to overcome the challenges posed by imbalanced datasets. In this work we propose the utilization of the APTx activation function to enhance our model’s forecasting ability. In addition, we compare the effectiveness of different combinations of activation functions, optimizers, and resampling techniques to identify configurations that yield promising results for predicting customer churn. Our results offer dual insights, enriching the existing literature in the field of hyperparameter selection, imbalanced learning, and churn prediction, while also revealing that APTx can be a promising component in the field of neural networks.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191687","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

Demand Forecasting New Fashion Products: A Review Paper 新时尚产品的需求预测：综述论文

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-09-05 DOI: 10.1002/for.3192

Anitha S., Neelakandan R.

引用次数: 0

Predictor Preselection for Mixed‐Frequency Dynamic Factor Models: A Simulation Study With an Empirical Application to GDP Nowcasting 混合频率动态因子模型的预测因子预选：模拟研究与 GDP 预报的经验应用

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-09-05 DOI: 10.1002/for.3193

Domenic Franjic, Karsten Schweikert

引用次数: 0

A multi‐objective optimization metaheuristic hybrid technique for forecasting the electricity consumption of the UAE: A grey wolf approach 预测阿联酋用电量的多目标优化元启发式混合技术：灰狼方法

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-08-23 DOI: 10.1002/for.3187

Andreas Karathanasopoulos, Chia Chun Lo, Mitra Sovan, Mohamed Osman, Hans‐Jörg von Mettenheim, Slim Skander

引用次数: 0

Toward a smart forecasting model in supply chain management: A case study of coffee in Vietnam 供应链管理中的智能预测模型：越南咖啡案例研究

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-08-19 DOI: 10.1002/for.3189

Thi Thuy Hanh Nguyen, Abdelghani Bekrar, Thi Muoi Le, Mourad Abed, Anirut Kantasa‐ard

{"title":"Toward a smart forecasting model in supply chain management: A case study of coffee in Vietnam","authors":"Thi Thuy Hanh Nguyen, Abdelghani Bekrar, Thi Muoi Le, Mourad Abed, Anirut Kantasa‐ard","doi":"10.1002/for.3189","DOIUrl":"https://doi.org/10.1002/for.3189","url":null,"abstract":"Forecasting is a crucial part of supply chain management. Accurate forecasts have a strong influence on supply chain performance. Many forecasting methods have been developed and adapted in various domains and industries. However, none are perfect in all contexts due to the data's characteristics and the methods' strength. Hence, we propose a new ARIMAX‐LSTM hybrid forecasting model that integrates ARIMAX and LSTM models to improve the ability to capture different combinations of linear and nonlinear patterns in time series. Our proposed model is validated in a case study of coffee demand in Vietnam. The case study results show that our proposed model outperforms the well‐known single and current hybrid models regarding performance measures and degree of association. Moreover, to prove the model's robustness, we test and compare our proposed model to the previous study for Thailand's agricultural products (pineapple, corn, and cassava). Computational results demonstrate that our hybrid model is superior in the majority of experiments. It has a strong capability of predicting complex time series data. Furthermore, our proposed method increases forecasting accuracy and enhances supply chain performance (measured by the bullwhip effect; net‐stock amplification, and transportation cost.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191689","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

Forecasting USD/RMB exchange rate using the ICEEMDAN‐CNN‐LSTM model 利用 ICEEMDAN-CNN-LSTM 模型预测美元兑人民币汇率

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-08-19 DOI: 10.1002/for.3190

Yun Zhou, Xuxu Zhu

{"title":"Forecasting USD/RMB exchange rate using the ICEEMDAN‐CNN‐LSTM model","authors":"Yun Zhou, Xuxu Zhu","doi":"10.1002/for.3190","DOIUrl":"https://doi.org/10.1002/for.3190","url":null,"abstract":"Because the exchange rate is essentially a dynamic and nonlinear system, exchange rate forecasting has been one of the most challenging topics in the financial field. This paper proposes a novel idea of “decomposition‐reconstruction‐integration” to predict exchange rate. First, based on ICEEMDAN, the original sequences are decomposed into multifrequency IMFs. Second, we use t‐test to determine the high‐frequency IMFs, low‐frequency IMFs, and trend sequence and reconstruct the high‐frequency IMFs into a new component sequence. Third, we use CNN‐LSTM model to predict these components separately and finally get the final prediction result by integration. This paper takes the USD/RMB exchange rate as research object, and the experimental results show that (1) the fluctuations of USD/RMB exchange rate are mainly affected by the trend sequence and low‐frequency IMFs and are less affected by high‐frequency IMFs. (2) The evaluation criterions RMSE, MAE, and MAPE of the ICEEMDAN‐CNN‐LSTM model are relatively small, with values of 0.0156, 0.0112, and 0.1679, respectively, indicating that the predictive performance of the model is optimal. (3) This paper has conducted various robust tests, all of which indicate that the proposed model has high prediction accuracy and stability. In summary, this paper has certain theoretical significance and application value.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191691","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 hybrid interval‐valued time series prediction model incorporating intuitionistic fuzzy cognitive map and fuzzy neural network 融合直觉模糊认知图谱和模糊神经网络的混合区间值时间序列预测模型

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-08-15 DOI: 10.1002/for.3181

Jiajia Zhang, Zhifu Tao, Jinpei Liu, Xi Liu, Huayou Chen

{"title":"A hybrid interval‐valued time series prediction model incorporating intuitionistic fuzzy cognitive map and fuzzy neural network","authors":"Jiajia Zhang, Zhifu Tao, Jinpei Liu, Xi Liu, Huayou Chen","doi":"10.1002/for.3181","DOIUrl":"https://doi.org/10.1002/for.3181","url":null,"abstract":"The definition of interval‐valued time series is now a valid tool that can be used to model uncertainty with known numerical bounds. However, how to provide accurate predictions of interval‐valued time series remains an open problem. The goal of this paper is to develop a hybrid interval‐valued time series prediction model that incorporates an intuitionistic fuzzy cognitive map and a fuzzy neural network. The causal relationship and adjacency matrix among nodes of the intuitionistic fuzzy cognitive map are defined and quantified using mutual subsethhood, in which the hesitation weight is added to the connection weight among concept nodes. The approach directly constructs concept nodes and a weight matrix for automatic recognition of intuitionistic fuzzy cognitive maps from original sequence data and combines the particle swarm optimization algorithm and back propagation algorithm to run with less manual intervention. The confidence intervals of forecasted interval values are also discussed. The developed prediction model is applied to forecast interval‐valued financial time series (i.e., the Nasdaq‐100 stock index), which is composed of daily minimum price and maximum price. The feasibility and validity of the proposed developed prediction model are shown through comparisons with some existing prediction models on interval‐valued time series.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191694","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

Forecasting Markov switching vector autoregressions: Evidence from simulation and application 马尔科夫切换向量自回归预测：模拟和应用证据

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-08-15 DOI: 10.1002/for.3180

Maddalena Cavicchioli

引用次数: 0

A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data 利用可解释的人工智能和大数据建立棉纱期货价格波动的新概率预测模型

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-08-15 DOI: 10.1002/for.3185

Huosong Xia, Xiaoyu Hou, Justin Zuopeng Zhang, Mohammad Zoynul Abedin

{"title":"A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data","authors":"Huosong Xia, Xiaoyu Hou, Justin Zuopeng Zhang, Mohammad Zoynul Abedin","doi":"10.1002/for.3185","DOIUrl":"https://doi.org/10.1002/for.3185","url":null,"abstract":"Cotton, cotton yarn, and other cotton products have frequent price volatility, increasing the difficulty for industry participants to develop rational business decision plans. To support cotton textile industry decision‐makers, we apply data mining methods to extract the main influencing factors affecting cotton yarn futures prices from big data and build a probabilistic forecasting model for cotton yarn price volatility with uncertainty assessment. Based on Explainable Artificial Intelligence (XAI) and data‐driven perspectives, we use the LassoNet algorithm to extract 18 features most relevant to the target variable from the massive data and visualize the importance values of the selected features to improve the reliability. Moreover, by combining conformal forecasting (CP) with quantile regression (QR), the uncertainty measure of the point estimation results of the long and short‐term memory (LSTM) model is applied to improve the application value of the model. Finally, SHAP (SHapley Additive exPlanations) is introduced to analyze the SHAP values of the input features on the output results and to explore in depth the interaction and mechanism of action between the input features and the target variables to improve the explainability of the model. Our model provides a “big data‐forecasting model‐decision support” decision paradigm for real‐world problems.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191692","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

Long‐term forecasting of maritime economics index using time‐series decomposition and two‐stage attention 利用时间序列分解和两阶段注意力对海洋经济指数进行长期预测

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-08-15 DOI: 10.1002/for.3176

Dohee Kim, Eunju Lee, Imam Mustafa Kamal, Hyerim Bae

{"title":"Long‐term forecasting of maritime economics index using time‐series decomposition and two‐stage attention","authors":"Dohee Kim, Eunju Lee, Imam Mustafa Kamal, Hyerim Bae","doi":"10.1002/for.3176","DOIUrl":"https://doi.org/10.1002/for.3176","url":null,"abstract":"Forecasting the maritime economics index, including container volume and Baltic Panamax Index, is essential for long‐term planning and decision‐making in the shipping industry. However, studies on container volume prediction are not sufficient, and the bulk freight index has highly fluctuating characteristics, which pose a challenge in long‐term prediction. This study proposes a new hybrid framework for the long‐term prediction of the maritime economics index. The framework consists of time‐series decomposition to break down a time‐series into several components (trend, seasonality, and residual), a two‐stage attention mechanism that prioritizes important variables to increase long‐term prediction accuracy and a long short‐term memory network that predicts and combines all components to derive the final predictive outcome. Extensive experiments are conducted using the container volume data, bulk freight index data, and various external variables. The proposed framework achieved a better predictive performance than existing time‐series methods, including conventional machine learning and deep learning‐based models, in the long‐term prediction of container volume and the Baltic Panamax Index. Hence, the proposed method can help in decision‐making through accurate long‐term predictions of the maritime economics index.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191690","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