International Journal of Forecasting最新文献_第6页

Interpretable water level forecaster with spatiotemporal causal attention mechanisms 基于时空因果注意机制的可解释水位预报

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2025-07-01 Epub Date: 2024-11-21 DOI: 10.1016/j.ijforecast.2024.10.003

Sungchul Hong , Yunjin Choi , Jong-June Jeon

{"title":"Interpretable water level forecaster with spatiotemporal causal attention mechanisms","authors":"Sungchul Hong , Yunjin Choi , Jong-June Jeon","doi":"10.1016/j.ijforecast.2024.10.003","DOIUrl":"10.1016/j.ijforecast.2024.10.003","url":null,"abstract":"<div><div>Accurate forecasting of river water levels is vital for effectively managing traffic flow and mitigating the risks associated with natural disasters. This task presents challenges due to the intricate factors influencing the flow of a river. Recent advances in machine learning have introduced numerous effective forecasting methods. However, these methods lack interpretability due to their complex structure, resulting in limited reliability. Addressing this issue, this study proposes a deep learning model that quantifies interpretability, with an emphasis on water level forecasting. This model focuses on generating quantitative interpretability measurements, which align with the common knowledge embedded in the input data. This is facilitated by the utilization of a transformer architecture that is purposefully designed with masking, incorporating a multi-layer network that captures spatiotemporal causation. We perform a comparative analysis on the Han River dataset obtained from Seoul, South Korea, from 2016 to 2021. The results illustrate that our approach offers enhanced interpretability consistent with common knowledge, outperforming competing methods. The approach also enhances robustness against distribution shift.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1037-1054"},"PeriodicalIF":6.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212021","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

A projected nonlinear state-space model for forecasting time series signals 一种预测时间序列信号的非线性状态空间模型

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2025-07-01 Epub Date: 2025-02-07 DOI: 10.1016/j.ijforecast.2025.01.002

Christian Donner , Anuj Mishra , Hideaki Shimazaki

引用次数: 0

Modeling and predicting failure in US credit unions 对美国信用合作社破产进行建模和预测

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2025-07-01 Epub Date: 2025-01-16 DOI: 10.1016/j.ijforecast.2024.12.004

Qiao Peng , Donal McKillop , Barry Quinn , Kailong Liu

引用次数: 0

Stock return predictability in the frequency domain 股票收益在频域的可预测性

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2025-07-01 Epub Date: 2025-01-16 DOI: 10.1016/j.ijforecast.2024.11.007

Zhifeng Dai , Fuwei Jiang , Jie Kang , Bowen Xue

引用次数: 0

Credit scoring model for fintech lending: An integration of large language models and FocalPoly loss 金融科技贷款的信用评分模型：大型语言模型和FocalPoly损失的集成

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2025-07-01 Epub Date: 2024-12-05 DOI: 10.1016/j.ijforecast.2024.07.005

Yufei Xia , Zhiyin Han , Yawen Li , Lingyun He

{"title":"Credit scoring model for fintech lending: An integration of large language models and FocalPoly loss","authors":"Yufei Xia , Zhiyin Han , Yawen Li , Lingyun He","doi":"10.1016/j.ijforecast.2024.07.005","DOIUrl":"10.1016/j.ijforecast.2024.07.005","url":null,"abstract":"<div><div>Fintech lending experiences high credit risk and needs an efficient credit scoring model, but it also faces limited data sources and severe class imbalance. We develop a novel two-stage credit scoring model (called LLM-FP-CatBoost) by solving the two issues simultaneously. Large language models (LLMs) initially extract narrative data as a supplementary credit dataset. A new FocalPoly loss is then incorporated with CatBoost to handle the class imbalance problem. Extensive comparisons demonstrate that the proposed LLM-FP-CatBoost significantly outperforms the benchmarks in most circumstances. When making pairwise comparisons between LLMs on the fintech lending dataset, we found that the Chinese-specific LLM, i.e., ERNIE 4.0, achieves the best overall performance, followed by GPT-4 and BERT-based models. The performance decomposition reveals that the superiority is mainly attributed to the new data source extracted by the LLMs. The SHAP algorithm further ensures the interpretability of LLM-FP-CatBoost. The superiority of the proposed LLM-FP-CatBoost model remains robust to hyperparameters of the loss function, specific LLMs, and other extraction methods of narrative data. Finally, we discuss some managerial implications concerning credit scoring in fintech lending.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 894-919"},"PeriodicalIF":6.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212028","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

Fundamental determinants of exchange rate expectations 汇率预期的基本决定因素

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2025-07-01 Epub Date: 2024-12-06 DOI: 10.1016/j.ijforecast.2024.09.004

Joscha Beckmann , Robert L. Czudaj

引用次数: 0

Multivariate dynamic mixed-frequency density pooling for financial forecasting 金融预测的多变量动态混频密度池

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2025-07-01 Epub Date: 2024-12-25 DOI: 10.1016/j.ijforecast.2024.11.011

Audronė Virbickaitė , Hedibert F. Lopes , Martina Danielova Zaharieva

引用次数: 0

SpotV2Net: Multivariate intraday spot volatility forecasting via vol-of-vol-informed graph attention networks SpotV2Net：通过volof - volinformed图形关注网络进行多变量日内现货波动预测

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2025-07-01 Epub Date: 2024-12-06 DOI: 10.1016/j.ijforecast.2024.11.004

Alessio Brini , Giacomo Toscano

{"title":"SpotV2Net: Multivariate intraday spot volatility forecasting via vol-of-vol-informed graph attention networks","authors":"Alessio Brini , Giacomo Toscano","doi":"10.1016/j.ijforecast.2024.11.004","DOIUrl":"10.1016/j.ijforecast.2024.11.004","url":null,"abstract":"<div><div>This paper introduces SpotV2Net, a multivariate intraday spot volatility forecasting model based on a graph attention network architecture. SpotV2Net represents assets as nodes within a graph and includes non-parametric high-frequency Fourier estimates of the spot volatility and co-volatility as node features. Further, it incorporates Fourier estimates of the spot volatility of volatility and co-volatility of volatility as features for node edges, to capture spillover effects. We test the forecasting accuracy of SpotV2Net in an extensive empirical exercise, conducted with the components of the Dow Jones Industrial Average index. The results we obtain suggest that SpotV2Net yields statistically significant gains in forecasting accuracy, for both single-step and multi-step forecasts, compared to a panel heterogeneous autoregressive model and alternative machine-learning models. To interpret the forecasts produced by SpotV2Net, we employ GNNExplainer (Ying et al., 2019), a model-agnostic interpretability tool, and thereby uncover subgraphs that are critical to a node’s predictions.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1093-1111"},"PeriodicalIF":6.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212024","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

Time-varying parameters as ridge regressions 时变参数作为脊回归

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2025-07-01 Epub Date: 2025-01-27 DOI: 10.1016/j.ijforecast.2024.08.006

Philippe Goulet Coulombe

引用次数: 0

SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe SolNet：全球光伏发电预测的开源深度学习模型

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2025-07-01 Epub Date: 2025-02-03 DOI: 10.1016/j.ijforecast.2024.12.003

Joris Depoortere, Johan Driesen, Johan Suykens, Hussain Syed Kazmi

{"title":"SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe","authors":"Joris Depoortere, Johan Driesen, Johan Suykens, Hussain Syed Kazmi","doi":"10.1016/j.ijforecast.2024.12.003","DOIUrl":"10.1016/j.ijforecast.2024.12.003","url":null,"abstract":"<div><div>Deep learning models have gained increasing prominence in recent years in solar photovoltaic (PV) forecasting. One drawback of these models is that they require a lot of high-quality data to perform well. This is often infeasible in practice, due to poor measurement infrastructure in legacy systems and the rapid build-up of new solar systems across the world. This paper proposes SolNet: a novel, general-purpose, multivariate solar power forecaster, which addresses these challenges by using a two-step forecasting pipeline that incorporates transfer learning from abundant synthetic data generated from PVGIS, before fine-tuning on observational data.</div><div>Using actual production data from hundreds of sites in The Netherlands, Australia, and Belgium, we show that SolNet improves forecasting performance over data-scarce settings as well as baseline models. We find transfer learning benefits to be the strongest when only limited observational data are available. At the same time, we provide several guidelines and considerations for transfer learning practitioners, as our results show that weather data, seasonal patterns, and possible misspecification in source location can have a major impact on the results. The SolNet models created in this way are applicable for any land-based solar photovoltaic system across the planet to obtain improved forecasting capabilities.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1223-1236"},"PeriodicalIF":6.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211941","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