International Journal of Forecasting最新文献

Forecasting house price growth rates with factor models and spatio-temporal clustering 利用要素模型和时空聚类预测房价增长率

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-10-10 DOI: 10.1016/j.ijforecast.2024.09.003

Raffaele Mattera , Philip Hans Franses

引用次数: 0

Forecasting realized volatility with spillover effects: Perspectives from graph neural networks 预测具有溢出效应的已实现波动率：图神经网络的视角

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-10-07 DOI: 10.1016/j.ijforecast.2024.09.002

Chao Zhang , Xingyue Pu , Mihai Cucuringu , Xiaowen Dong

{"title":"Forecasting realized volatility with spillover effects: Perspectives from graph neural networks","authors":"Chao Zhang , Xingyue Pu , Mihai Cucuringu , Xiaowen Dong","doi":"10.1016/j.ijforecast.2024.09.002","DOIUrl":"10.1016/j.ijforecast.2024.09.002","url":null,"abstract":"<div><div>We present a novel nonparametric methodology for modeling and forecasting multivariate realized volatilities using customized graph neural networks to incorporate spillover effects across stocks. The proposed model offers the benefits of incorporating spillover effects from multi-hop neighbors, capturing nonlinear relationships, and flexible training with different loss functions. The empirical findings suggest that incorporating spillover effects from multi-hop neighbors alone does not yield a clear advantage in terms of predictive accuracy. Furthermore, modeling nonlinear spillover effects enhances the forecasting accuracy of realized volatilities, particularly for short-term horizons of up to one week. More importantly, our results consistently indicate that training with the quasi-likelihood loss leads to substantial improvements in model performance compared to the commonly used mean squared error, primarily due to its superior handling of heteroskedasticity. A comprehensive series of empirical evaluations in alternative settings confirm the robustness of our results.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 377-397"},"PeriodicalIF":6.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Sparse time-varying parameter VECMs with an application to modeling electricity prices 稀疏时变参数 VECMs 在电价建模中的应用

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-09-26 DOI: 10.1016/j.ijforecast.2024.09.001

Niko Hauzenberger , Michael Pfarrhofer , Luca Rossini

引用次数: 0

Guest editorial: Forecasting for social good 特邀社论：社会公益预测

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-09-20 DOI: 10.1016/j.ijforecast.2024.08.007

Bahman Rostami-Tabar, Pierre Pinson, Michael D. Porter

引用次数: 0

On memory-augmented gated recurrent unit network 关于记忆增强型门控递归单元网络

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2024-08-31 DOI: 10.1016/j.ijforecast.2024.07.008

Maolin Yang, Muyi Li, Guodong Li

引用次数: 0

A framework for timely and accessible long-term forecasting of shale gas production based on time series pattern matching 基于时间序列模式匹配的页岩气产量长期及时预测框架

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2024-08-24 DOI: 10.1016/j.ijforecast.2024.07.009

Yilun Dong, Youzhi Hao, Detang Lu

{"title":"A framework for timely and accessible long-term forecasting of shale gas production based on time series pattern matching","authors":"Yilun Dong, Youzhi Hao, Detang Lu","doi":"10.1016/j.ijforecast.2024.07.009","DOIUrl":"https://doi.org/10.1016/j.ijforecast.2024.07.009","url":null,"abstract":"Shale gas production forecasting is an important research topic in the gas industry. A common shale gas block includes dozens or even thousands of wells and therefore has a great number of historical production series. However, most existing methods apply single-well modelling. This cannot exploit data from other wells and requires a long production history from the target well, so the forecasting timeliness is compromised. Moreover, the parameters required by many of the existing methods are difficult to collect in practice, so the forecasting accessibility is compromised. Therefore, this study presents a shale gas production forecasting framework with improved timeliness and accessibility. To ensure timeliness, the proposed approach utilises historical data from existing wells and only requires a short production history from the target well. To ensure accessibility, the proposed approach only requires past daily production time and gas yield. The performance of the proposed method is demonstrated through a comparison with baseline methods. The results regarding cumulative gas production forecasting indicate that the proposed method has an average overall mean absolute percentage error (OMAPE) of 0.210, outperforming an artificial neural network with an average OMAPE of 0.241 and ARIMA with an average OMAPE of more than 2.","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"3 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 mail flow: A hierarchical approach for enhanced societal wellbeing 预测邮件流量：提高社会福祉的分层方法

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-07-29 DOI: 10.1016/j.ijforecast.2024.07.001

Nadine Kafa, M. Zied Babai, Walid Klibi

{"title":"Forecasting mail flow: A hierarchical approach for enhanced societal wellbeing","authors":"Nadine Kafa, M. Zied Babai, Walid Klibi","doi":"10.1016/j.ijforecast.2024.07.001","DOIUrl":"10.1016/j.ijforecast.2024.07.001","url":null,"abstract":"<div><div>Forecasting for Social Good has gained considerable attention for its impact on individuals, businesses, and society. This research introduces an integrated hierarchical forecasting-based decision-making approach for mail flow in a major postal organisation, presenting new social performance indicators. These indicators, including the discharge level, discharge rate, and overload rate, guide decision makers toward consistent workload planning, bridging a literature gap concerning forecast utility measures. The study evaluates three forecasting methods—exponential smoothing with error, trend, and seasonality (ETS), the autoregressive integrated moving average (ARIMA), and the light gradient boosting machine (LightGBM)—in terms of forecast accuracy and social measures, comparing them to the organisation’s current method. The empirical results confirm that the proposed approach is more accurate than the current method. Moreover, while ETS shows the highest forecast accuracy, LightGBM outperforms all methods in social measures. This indicates that a highly accurate forecasting method does not always enhance social performance, challenging traditional views on forecasting evaluation.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 51-65"},"PeriodicalIF":6.9,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Predicting and optimizing the fair allocation of donations in hunger relief supply chains 预测和优化饥饿救济供应链中捐款的公平分配

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-07-10 DOI: 10.1016/j.ijforecast.2024.06.004

Nowshin Sharmile , Isaac A. Nuamah , Lauren Davis , Funda Samanlioglu , Steven Jiang , Carter Crain

引用次数: 0

A modified VAR-deGARCH model for asynchronous multivariate financial time series via variational Bayesian inference 通过变异贝叶斯推理建立异步多变量金融时间序列的修正 VAR-deGARCH 模型

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-06-21 DOI: 10.1016/j.ijforecast.2024.06.002

Wei-Ting Lai , Ray-Bing Chen , Shih-Feng Huang

引用次数: 0

ABC-based forecasting in misspecified state space models 基于ABC的失范状态空间模型预测

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-06-19 DOI: 10.1016/j.ijforecast.2024.05.005

Chaya Weerasinghe, Rubén Loaiza-Maya, Gael M. Martin, David T. Frazier

{"title":"ABC-based forecasting in misspecified state space models","authors":"Chaya Weerasinghe, Rubén Loaiza-Maya, Gael M. Martin, David T. Frazier","doi":"10.1016/j.ijforecast.2024.05.005","DOIUrl":"10.1016/j.ijforecast.2024.05.005","url":null,"abstract":"<div><div>Approximate Bayesian Computation (ABC) has gained popularity as a method for conducting inference and forecasting in complex models, most notably those which are intractable in some sense. In this paper, we use ABC to produce probabilistic forecasts in state space models (SSMs). Whilst ABC-based forecasting in correctly-specified SSMs has been studied, the misspecified case has not been investigated. It is this case that we emphasize. We invoke recent principles of ‘focused’ Bayesian prediction, whereby Bayesian updates are driven by a scoring rule that rewards predictive accuracy; the aim being to produce predictives that perform well in that rule, despite misspecification. Two methods are investigated for producing the focused predictions. In a simulation setting, ‘coherent’ predictions are in evidence for both methods. That is, the predictive constructed using a particular scoring rule often predicts best according to that rule. Importantly, both focused methods typically produce more accurate forecasts than an exact but misspecified predictive, in particular when the degree of misspecification is marked. An empirical application to a truly intractable SSM completes the paper.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 270-289"},"PeriodicalIF":6.9,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0