International Journal of Forecasting最新文献

Acknowledgement to reviewers 审稿人致谢

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2025-02-17 DOI: 10.1016/j.ijforecast.2025.02.003

引用次数: 0

Introduction to the Special Issue on Judgment in Forecasting 《预测判断》专刊导论

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2025-02-07 DOI: 10.1016/j.ijforecast.2025.01.004

Robert Fildes, Fergus Bolger, Paul Goodwin, Nigel Harvey, Matthias Seifert

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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 : 2025-02-07 DOI: 10.1016/j.ijforecast.2025.01.002

Christian Donner , Anuj Mishra , Hideaki Shimazaki

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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 : 2025-02-03 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":"2025-02-03","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

Real-time monitoring procedures for early detection of bubbles 实时监测程序，尽早发现气泡

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2025-01-27 DOI: 10.1016/j.ijforecast.2024.12.005

E.J. Whitehouse , D.I. Harvey , S.J. Leybourne

{"title":"Real-time monitoring procedures for early detection of bubbles","authors":"E.J. Whitehouse , D.I. Harvey , S.J. Leybourne","doi":"10.1016/j.ijforecast.2024.12.005","DOIUrl":"10.1016/j.ijforecast.2024.12.005","url":null,"abstract":"<div><div>Asset price bubbles and crashes can have severe consequences for the stability of financial and economic systems. Policymakers require timely identification of such bubbles in order to respond to their emergence. In this paper we propose new econometric procedures that improve the speed of detection for an emerging asset price bubble in real time. Our new monitoring procedures make use of alternative variance standardisations that are better able to capture the behaviour of the underlying process during a bubble phase. We derive asymptotic results to show that using these alternative variance standardisations does not increase the probability of false detection under the no-bubble (unit root) null hypothesis relative to existing procedures. However, Monte Carlo simulations demonstrate that much earlier detection becomes possible with our new procedures under the bubble (explosive autoregressive) alternative. Empirical applications to OECD housing markets and bitcoin prices show the value in terms of earlier detection of bubbles that our new procedures can achieve. In particular, we show that the United States housing bubble that preceded the global financial crisis could have been detected as early as 1999:Q1 by our new procedures.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1260-1277"},"PeriodicalIF":6.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211943","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 : 2025-01-27 DOI: 10.1016/j.ijforecast.2024.08.006

Philippe Goulet Coulombe

引用次数: 0

Predicting value at risk for cryptocurrencies with generalized random forests 用广义随机森林预测加密货币的风险值

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2025-01-24 DOI: 10.1016/j.ijforecast.2024.12.002

Rebekka Buse , Konstantin Görgen , Melanie Schienle

引用次数: 0

How does training improve individual forecasts? Modeling differences in compensatory and non-compensatory biases in geopolitical forecasts 培训如何提高个人预测？地缘政治预测中补偿性和非补偿性偏差的建模差异

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2025-01-22 DOI: 10.1016/j.ijforecast.2024.12.001

Vahid Karimi Motahhar , Thomas S. Gruca

引用次数: 0

Forecasting stock market return with anomalies: Evidence from China 用异常预测股市回报：来自中国的证据

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2025-01-21 DOI: 10.1016/j.ijforecast.2024.12.007

Jianqiu Wang , Zhuo Wang , Ke Wu

引用次数: 0

Subjective-probability forecasts of existential risk: Initial results from a hybrid persuasion-forecasting tournament 存在风险的主观概率预测：混合劝说-预测比赛的初步结果

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2025-01-17 DOI: 10.1016/j.ijforecast.2024.11.008

Ezra Karger , Josh Rosenberg , Zachary Jacobs , Molly Hickman , Phillip E. Tetlock

{"title":"Subjective-probability forecasts of existential risk: Initial results from a hybrid persuasion-forecasting tournament","authors":"Ezra Karger , Josh Rosenberg , Zachary Jacobs , Molly Hickman , Phillip E. Tetlock","doi":"10.1016/j.ijforecast.2024.11.008","DOIUrl":"10.1016/j.ijforecast.2024.11.008","url":null,"abstract":"<div><div>A multi-stage persuasion-forecasting tournament asked specialists and generalists (“superforecasters”) to explain their probability judgments of short- and long-run existential threats to humanity. Specialists were more pessimistic, especially on long-run threats posed by artificial intelligence (AI). Despite incentives to share their best arguments during four months of discussion, neither side materially moved the other’s views. This would be puzzling if participants were Bayesian agents methodically sifting through elusive clues about distant futures but it is less puzzling if participants were boundedly rational agents searching for confirmatory evidence as the risks of embarrassing accuracy feedback receded. Consistent with the latter mechanism, strong AI-risk proponents made particularly extreme long- but not short-range forecasts and over-estimated the long-range AI-risk forecasts of others. We stress the potential of these methods to inform high-stakes debates, but we acknowledge limits on what even skilled forecasters can achieve in anticipating rare or unprecedented events.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 499-516"},"PeriodicalIF":6.9,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579420","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