International Journal of Forecasting最新文献_第3页

Avoiding overconfidence: Evidence from the M6 financial competition 避免过度自信：来自M6金融竞争的证据

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-10-01 Epub Date: 2024-11-04 DOI: 10.1016/j.ijforecast.2024.10.001

Spyros Makridakis , Evangelos Spiliotis , Maria Michailidis

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 : 2025-10-01 Epub 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":"2025-10-01","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 : 2025-10-01 Epub 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":"2025-10-01","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

The M6 forecasting competition: Bridging the gap between forecasting and investment decisions M6预测竞赛：弥合预测和投资决策之间的差距

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-10-01 Epub Date: 2024-11-30 DOI: 10.1016/j.ijforecast.2024.11.002

Spyros Makridakis , Evangelos Spiliotis , Ross Hollyman , Fotios Petropoulos , Norman Swanson , Anil Gaba

{"title":"The M6 forecasting competition: Bridging the gap between forecasting and investment decisions","authors":"Spyros Makridakis , Evangelos Spiliotis , Ross Hollyman , Fotios Petropoulos , Norman Swanson , Anil Gaba","doi":"10.1016/j.ijforecast.2024.11.002","DOIUrl":"10.1016/j.ijforecast.2024.11.002","url":null,"abstract":"<div><div>The M6 forecasting competition, the sixth in the Makridakis competition sequence, focused on financial forecasting. A key objective of the M6 competition was to contribute to the debate surrounding the Efficient Market Hypothesis by examining how and why market participants make investment decisions. To address these objectives, the M6 competition investigated forecasting accuracy and investment performance in a universe of 100 publicly traded assets. The competition employed live evaluation on real data across multiple periods, a cross-sectional setting where participants predicted asset performance relative to that of other assets, and a direct evaluation of the utility of forecasts. In this way, we were able to measure the benefits of accurate forecasting and assess the importance of forecasting when making investment decisions. Our findings highlight the challenges that participants faced when attempting to accurately forecast the relative performance of assets, the great difficulty associated with trying to consistently outperform the market, the limited connection between submitted forecasts and investment decisions, the value added by information exchange and the “wisdom of crowds”, and the value of utilizing risk models when attempting to connect prediction and investing decisions.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1315-1354"},"PeriodicalIF":7.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019329","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

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 : 2025-10-01 Epub 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":"2025-10-01","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

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 : 2025-10-01 Epub 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":"2025-10-01","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

Unraveling the effect of engagement and consistency in the results of the M6 forecasting competition 在M6预测竞赛结果中揭示参与和一致性的影响

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-10-01 Epub Date: 2025-05-14 DOI: 10.1016/j.ijforecast.2025.04.002

Anastasios Kaltsounis, Evangelos Theodorou, Evangelos Spiliotis, Vassilios Assimakopoulos

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 : 2025-10-01 Epub Date: 2025-02-26 DOI: 10.1016/j.ijforecast.2025.02.001

Luis Gruber, Gregor Kastner

引用次数: 0

Probabilistic forecasting of cross-sectional returns: A Bayesian dynamic factor model with heteroskedasticity 横截面回报的概率预测：具有异方差性的贝叶斯动态因子模型

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-10-01 Epub Date: 2024-07-12 DOI: 10.1016/j.ijforecast.2024.06.007

Dan Weitzenfeld

引用次数: 0

Adaptive forecasting in dynamic markets: An evaluation of AutoTS within the M6 competition 动态市场中的自适应预测：AutoTS在M6竞争中的评价

IF 7.1 2区经济学

International Journal of Forecasting Pub Date : 2025-10-01 Epub Date: 2025-08-28 DOI: 10.1016/j.ijforecast.2025.08.004

Colin Catlin

{"title":"Adaptive forecasting in dynamic markets: An evaluation of AutoTS within the M6 competition","authors":"Colin Catlin","doi":"10.1016/j.ijforecast.2025.08.004","DOIUrl":"10.1016/j.ijforecast.2025.08.004","url":null,"abstract":"<div><div>In contemporary forecasting, the challenges of navigating the intricacies of erratic human-induced patterns combine with the challenges of navigating the overwhelming number of methods and models available to manage these data. The M6 Competition, which emphasized repeated, real-time monthly forecasting of stock markets, featured many of these difficulties. Here, AutoTS, an open-source Python package designed specifically for probabilistic time series predictions, is evaluated within the context of this competition. AutoTS includes an extensive repertoire of models, augmented by robust data preprocessing utilities, and employs genetic algorithms to fine-tune model parameters, contingent upon user-delineated evaluation metrics. This study describes the deployment of AutoTS in the M6 Competition, which won the investment decision challenge, and outlines the model selection pipeline and the process of converting forecasts into decisions which produced this result. Although a single definitive model remains elusive, these findings underscore the potential value of methodologies that are dynamic and largely autonomous.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 4","pages":"Pages 1485-1493"},"PeriodicalIF":7.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020565","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