International Journal of Forecasting最新文献

英文中文

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 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-03-05 DOI: 10.1016/j.ijforecast.2025.02.004

Rodrigo Alves

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 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 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.

足球（也被称为足球或协会足球）是世界上最受欢迎的运动。它是技巧和运气的结合，这使得它非常不可预测。为了解决这种不可预测性，在过去十年中，利用机器学习技术来预测足球相关特征的流行程度激增。这一趋势与足球分析日益职业化的趋势相一致。尽管取得了这些进展，但现有的工作仍处于早期阶段，缺乏捕捉这项运动复杂细微差别所需的深度。在这项研究中，我们引入了一种卷积方法，旨在预测足球比赛中下一个事件的发生，例如进球或角球，仅依赖于易于访问的过去事件进行预测。我们的方法采用在线方法，这意味着可以在现场比赛中计算预测结果。为了验证我们的方法，我们利用来自各个欧洲精英足球联赛的数据，对五个基线模型进行了全面的评估。此外，还进行了消融研究，以了解我们方法的潜在机制。最后，我们介绍了我们提出的方法的实际应用和可解释方面。

{"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 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-02-26 DOI: 10.1016/j.ijforecast.2025.02.001

Luis Gruber, Gregor Kastner

Vector autoregressions (VARs) are widely applied when it comes to modeling and forecasting macroeconomic variables. In high dimensions, however, they are prone to overfitting. Bayesian methods—more concretely, shrinkage priors—have been shown to be successful at improving prediction performance. In the present paper, we introduce the semi-global framework, in which we replace the traditional global shrinkage parameter with group-specific shrinkage parameters. We show how this framework can be applied to various shrinkage priors, such as global–local priors and stochastic search variable selection priors. We demonstrate the virtues of the proposed framework in an extensive simulation study and in an empirical application forecasting data on the US economy. Further, we shed more light on the ongoing ‘illusion of sparsity’ debate, finding that forecasting performances under sparse/dense priors vary across evaluated economic variables and across time frames. Dynamic model averaging, however, can combine the merits of both worlds.

向量自回归（var）在宏观经济变量建模和预测方面有着广泛的应用。然而，在高维情况下，它们容易出现过拟合。贝叶斯方法——更具体地说，收缩先验——已被证明在提高预测性能方面是成功的。在本文中，我们引入了半全局框架，在该框架中，我们用特定于群体的收缩参数取代了传统的全局收缩参数。我们展示了该框架如何应用于各种收缩先验，例如全局-局部先验和随机搜索变量选择先验。我们在广泛的模拟研究和美国经济预测数据的实证应用中证明了所提出框架的优点。此外，我们进一步阐明了正在进行的“稀疏性错觉”辩论，发现稀疏/密集先验下的预测性能在评估的经济变量和时间框架中有所不同。然而，动态模型平均可以将两者的优点结合起来。

引用次数: 0

IF 7.1 2区经济学 Q1 ECONOMICS

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 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-02-25 DOI: 10.1016/j.ijforecast.2025.01.005

Filip Staněk

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 backpropagation, 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.

本文介绍了在M6比赛的预测和投资挑战中分别获得第四名和第六名的方法，最终确保了两项全能总排名的第一名。在预测挑战中，我们测试了一种新的元学习模型，该模型利用超网络设计了一个针对特定预测任务的参数化模型。这种方法允许我们利用在单个预测任务（例如，资产）中观察到的相似性，同时也承认其数据生成过程中的潜在异质性。模型的训练可以直接通过反向传播进行，不需要依赖高阶导数，相当于同时搜索参数函数及其最优参数值的空间。该模型的功能超出M6，在正弦回归任务中优于最先进的元学习方法，并且在M4预测竞争中的时间序列上优于传统参数模型。在投资挑战中，我们根据当前排名调整投资组合权重，以诱导我们的提交与其他参与者的提交之间或大或小的相关性，旨在最大限度地提高获得好排名的可能性。虽然这种投资组合策略可以增加获得有利排名的可能性，但它自相矛盾地表现出负的预期回报。

{"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 Q1 ECONOMICS

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

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.

预测通常在业务上下文中定期生成，以做出下游决策。在这里，预测不仅要尽可能准确，而且不能随意改变，在某种意义上要稳定。在本文中，我们探讨了两种类型的预测稳定性，我们称之为垂直稳定性（对于同一目标的不同来源的预测）和水平稳定性（对于来自同一来源的不同目标的预测）。现有文献中的工作只适用于某些基本模型，并且只能垂直稳定预测。我们提出了一种简单的基于线性插值的方法来稳定任何基本模型提供的预测，包括垂直和水平。我们的方法在稳定性和准确性之间做出了明确的权衡，在这种权衡的范围内的任何一点产生预测。我们使用N-BEATS、pooled regression、LightGBM、ETS和ARIMA作为基础模型，在四个公开可用的数据集上对不同的误差和稳定性进行评估。在一些数据集上，提出的框架实现了比基本预测更准确和更稳定的预测。在其他方面，我们的预测准确度略低，但稳定得多。

{"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 Q1 ECONOMICS

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 Q1 ECONOMICS

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

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.

对超级预测者的判断预测研究表明，个体的预测能力是不同的。然而，这项工作的基本概念侧重于准确性，并没有充分纳入预见的时间维度。我们将远见重新定义为准确预测世界未来状态的能力，随着时间的推移，准确性变得越来越重要。为了在预测比赛中实现预见性，我们提出了各种严格适当的评分规则，并使用模拟研究和现实世界的预测数据将它们与现有的评分规则进行比较，这些预测数据由9694名预测员在四年地缘政治预测比赛的498个问题上的414,168个分数组成。结果表明，线性时间加权Brier分数应该是预见性的默认操作化，而先前研究提出的概率训练和团队干预可能不会像我们概念化的那样提高预见性。我们通过澄清预测的概念、操作和相关关系，为判断预测的研究做出贡献。

{"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 Q1 ECONOMICS

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 Q1 ECONOMICS

International Journal of Forecasting

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

Christian Donner , Anuj Mishra , Hideaki Shimazaki

Learning and forecasting stochastic time series is essential in various scientific fields. However, despite the proposals of nonlinear filters and deep-learning methods, it remains challenging to capture nonlinear dynamics from a few noisy samples and predict future trajectories with uncertainty estimates while maintaining computational efficiency. Here, we propose a fast algorithm to learn and forecast nonlinear dynamics from noisy time series data. A key feature of the proposed model is kernel functions applied to projected lines, enabling the fast and efficient capture of nonlinearities in the latent dynamics. Through empirical case studies and benchmarking, the model demonstrates its effectiveness at learning and forecasting complex nonlinear dynamics, offering a valuable tool for researchers and practitioners in time series analysis.

学习和预测随机时间序列在许多科学领域都是必不可少的。然而，尽管提出了非线性滤波器和深度学习方法，但从少量噪声样本中捕获非线性动力学并在保持计算效率的同时使用不确定性估计预测未来轨迹仍然具有挑战性。在这里，我们提出了一种快速的算法来学习和预测非线性动态从噪声时间序列数据。该模型的一个关键特征是将核函数应用于投影线，从而能够快速有效地捕获潜在动力学中的非线性。通过实证案例研究和基准测试，该模型证明了其在学习和预测复杂非线性动力学方面的有效性，为时间序列分析的研究人员和实践者提供了有价值的工具。

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 Q1 ECONOMICS

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

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.

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.

近年来，深度学习模型在太阳能光伏（PV）预测领域得到了越来越多的关注。这些模型的一个缺点是，它们需要大量高质量的数据才能运行良好。这在实践中往往是不可行的，因为遗留系统的测量基础设施很差，而且世界各地正在迅速建立新的太阳能系统。本文提出了SolNet：一种新颖的、通用的、多元的太阳能预测器，它通过使用两步预测管道来解决这些挑战，该管道结合了从PVGIS生成的丰富合成数据的迁移学习，然后对观测数据进行微调。使用来自荷兰、澳大利亚和比利时数百个站点的实际生产数据，我们表明SolNet提高了数据稀缺设置和基线模型的预测性能。我们发现，只有有限的观测数据可用时，迁移学习的好处是最强的。同时，我们为迁移学习实践者提供了一些指导方针和注意事项，因为我们的结果表明，天气数据、季节模式和源位置可能的错误说明会对结果产生重大影响。以这种方式创建的SolNet模型适用于地球上任何陆基太阳能光伏系统，以获得改进的预测能力。

{"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

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

International Journal of Forecasting

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀