International Journal of Forecasting最新文献_第2页

SCORE: A convolutional approach for football event forecasting SCORE：一个用于足球赛事预测的卷积方法

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-03-05 DOI: 10.1016/j.ijforecast.2025.02.004

Rodrigo Alves

{"title":"SCORE: A convolutional approach for football event forecasting","authors":"Rodrigo Alves","doi":"10.1016/j.ijforecast.2025.02.004","DOIUrl":"10.1016/j.ijforecast.2025.02.004","url":null,"abstract":"<div><div>Football (also known as soccer or association football) is the most popular sport in the world. It is a blend of skill and luck, making it highly unpredictable. To address this unpredictability, there has been a surge in popularity over the past decade in employing machine learning techniques<span><span> for forecasting football-related features. This trend aligns with the growing professionalism in football analytics. Despite this progress, the existing body of work remains in its early stages, lacking the depth required to capture the intricate nuances of the sport. In this study, we introduce a convolutional approach designed to predict the occurrence of the next event in a football match, such as a goal or a corner kick, relying solely on easy-to-access past events for predictions. Our methodology adopts an online approach, meaning predictions can be computed during a live match. To validate our approach, we conduct a comprehensive evaluation against five </span>baseline models, utilizing data from various elite European football leagues. Additionally, an ablation study is performed to understand the underlying mechanisms of our method. Finally, we present practical applications and interpretable aspects of our proposed approach.</span></div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1636-1652"},"PeriodicalIF":7.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019322","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 macroeconomic data with Bayesian VARs: Sparse or dense? It depends! 用贝叶斯var预测宏观经济数据：稀疏还是密集？视情况而定!

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-02-26 DOI: 10.1016/j.ijforecast.2025.02.001

Luis Gruber, Gregor Kastner

引用次数: 0

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-02-26 DOI: 10.1016/j.ijforecast.2025.02.005

Giorgio Corani

引用次数: 0

Designing time-series models with hypernetworks and adversarial portfolios 设计具有超网络和对抗性组合的时间序列模型

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-02-25 DOI: 10.1016/j.ijforecast.2025.01.005

Filip Staněk

{"title":"Designing time-series models with hypernetworks and adversarial portfolios","authors":"Filip Staněk","doi":"10.1016/j.ijforecast.2025.01.005","DOIUrl":"10.1016/j.ijforecast.2025.01.005","url":null,"abstract":"<div><div><span>This article describes the methods that achieved fourth and sixth place in the forecasting and investment challenges, respectively, of the M6 competition, ultimately securing first place in the overall duathlon ranking. In the forecasting challenge, we tested a novel meta-learning model that utilizes hypernetworks to design a parametric model tailored to a specific family of forecasting tasks. This approach allowed us to leverage similarities observed across individual forecasting tasks (i.e., assets) while also acknowledging potential heterogeneity in their data generating processes. The model’s training can be directly performed with </span>backpropagation<span>, eliminating the need to rely on higher-order derivatives, and is equivalent to a simultaneous search over the space of parametric functions and their optimal parameter values. The proposed model’s capabilities extend beyond M6, demonstrating superiority over state-of-the-art meta-learning methods in the sinusoidal regression task and outperforming conventional parametric models on time series from the M4 forecasting competition. In the investment challenge, we adjusted portfolio weights to induce greater or smaller correlation between our submission and that of other participants, depending on the current ranking, aiming to maximize the probability of achieving a good rank. While this portfolio strategy can increase the probability of securing a favorable rank, it paradoxically exhibits negative expected returns.</span></div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1461-1476"},"PeriodicalIF":7.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020564","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

On forecast stability 论预报稳定性

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-02-25 DOI: 10.1016/j.ijforecast.2025.01.006

Rakshitha Godahewa , Christoph Bergmeir , Zeynep Erkin Baz , Chengjun Zhu , Zhangdi Song , Salvador García , Dario Benavides

{"title":"On forecast stability","authors":"Rakshitha Godahewa , Christoph Bergmeir , Zeynep Erkin Baz , Chengjun Zhu , Zhangdi Song , Salvador García , Dario Benavides","doi":"10.1016/j.ijforecast.2025.01.006","DOIUrl":"10.1016/j.ijforecast.2025.01.006","url":null,"abstract":"<div><div>Forecasts are typically produced in a business context on a regular basis to make downstream decisions. Here, forecasts should not only be as accurate as possible, but also should not change arbitrarily, and be stable in some sense. In this paper, we explore two types of forecast stability that we call vertical stability (for forecasts from different origins for the same target) and horizontal stability (for forecasts from the same origin for different targets). Existing works in the literature are only applicable to certain base models and can only stabilise forecasts vertically. We propose a simple linear-interpolation-based approach to stabilise the forecasts provided by any base model, both vertically and horizontally. Our method makes the trade-off between stability and accuracy explicit, producing forecasts at any point in the spectrum of this trade-off. We used N-BEATS, pooled regression, LightGBM, ETS, and ARIMA as base models in our evaluation across different error and stability measures on four publicly available datasets. On some datasets, the proposed framework achieved forecasts that were both more accurate and stable than the base forecasts. On the others, we achieved forecasts that were slightly less accurate but much more stable.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1539-1558"},"PeriodicalIF":7.1,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020569","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

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

Individual foresight: Concept, operationalization, and correlates 个人远见：概念、操作化和相关关系

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-02-08 DOI: 10.1016/j.ijforecast.2025.01.003

Benedikt Alexander Schuler , Johann Peter Murmann , Marie Beisemann , Ville Satopää

{"title":"Individual foresight: Concept, operationalization, and correlates","authors":"Benedikt Alexander Schuler , Johann Peter Murmann , Marie Beisemann , Ville Satopää","doi":"10.1016/j.ijforecast.2025.01.003","DOIUrl":"10.1016/j.ijforecast.2025.01.003","url":null,"abstract":"<div><div>Judgmental forecasting research on superforecasters has demonstrated that individuals differ in their foresight. However, the concept underlying this work focuses on accuracy and does not fully incorporate the time dimension of foresight. We reconceptualize foresight as the ability to predict future states of the world accurately, where accuracy becomes continuously more important over time. To operationalize foresight in forecasting tournaments, we propose various strictly proper scoring rules and compare them with existing scoring rules using a simulation study and real-world forecasting data consisting of 414,168 scores for 9694 forecasters on 498 questions from a four-year geopolitical forecasting tournament. The results suggest that the linear time-weighted Brier score should be the default operationalization of foresight and that probability training and teaming interventions as proposed by prior research may not improve foresight as we conceptualize it. We contribute to judgmental forecasting research by clarifying the concept, operationalization, and correlates of foresight.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1521-1538"},"PeriodicalIF":7.1,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020568","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

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

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

Christian Donner , Anuj Mishra , Hideaki Shimazaki

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 : 2025-02-03 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