International Journal of Forecasting最新文献

英文中文

Hierarchical neural additive models for interpretable demand forecasts 可解释需求预测的层次神经加性模型

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-04-30 DOI: 10.1016/j.ijforecast.2025.03.003

Leif Feddersen, Catherine Cleophas

Demand forecasts are the basis for numerous business decisions, ranging from inventory management to strategic facility planning. While machine learning approaches offer accuracy gains, they notoriously lack interpretability and acceptance. To address this dilemma, we introduce hierarchical neural additive models (HNAMs) for time series. HNAMs expand upon neural additive models by introducing a time-series-specific additive model consisting of level and covariate effects. Covariates may interact only according to a user-specified hierarchy. For example, given the hierarchy weekday, holiday, promotion, weekday effects are estimated independently, whereas a holiday effect depends on the weekday, and a promotional effect is conditioned on both the weekday and holiday. Thereby, HNAMs clearly attribute additive effects to their respective covariates, enabling intuitive forecasting interfaces with which analysts can interact. We provide benchmarks against established machine learning and statistical models on real-world data to reveal HNAMs’ competitive accuracy.

需求预测是许多商业决策的基础，从库存管理到战略设施规划。虽然机器学习方法可以提高准确性，但众所周知，它们缺乏可解释性和可接受性。为了解决这一难题，我们引入了时间序列的层次神经加性模型（HNAMs）。HNAMs通过引入一个由水平和协变量效应组成的时间序列特定的加性模型来扩展神经加性模型。协变量只能根据用户指定的层次结构进行交互。例如，给定工作日、假日、促销的层次结构，工作日效应是独立估计的，而假日效应取决于工作日，促销效应取决于工作日和假日。因此，HNAMs清楚地将可加性效应归因于它们各自的协变量，从而实现了分析师可以与之交互的直观预测界面。我们针对已建立的机器学习和真实世界数据的统计模型提供基准，以揭示HNAMs的竞争准确性。

引用次数: 0

M6 investment challenge: The role of luck and strategic considerations M6投资挑战：运气的作用和战略考虑

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-04-30 DOI: 10.1016/j.ijforecast.2025.03.005

Filip Staněk

This article investigates the influence of luck and strategic considerations on the performance of teams participating in the M6 investment challenge. We find that there is insufficient evidence to suggest that the extreme Sharpe ratios observed are beyond what one would expect by chance, given the number of teams, and thus not necessarily indicative of the possibility of consistently attaining abnormal returns. These findings are consistent with the efficient-market hypothesis, reinforcing the notion that any apparent outperformance is indistinguishable from statistical noise. Furthermore, we introduce a stylized model of the competition to derive and analyze a portfolio strategy optimized for attaining the top rank. The results demonstrate that the task of achieving the top rank is not necessarily identical to that of attaining the best investment returns in expectation. It is possible to improve one’s chances of winning, even without the ability to attain abnormal returns, by constructing a portfolio that deviates from the strategies of competitors. Empirical analysis of submitted portfolios shows that teams that differentiated themselves from competitors by holding a higher proportion of short positions were more than eight times as likely to secure a top rank, aligning with findings from the stylized model.

本文研究运气和战略考虑对参与M6投资挑战的团队绩效的影响。我们发现，没有足够的证据表明，观察到的极端夏普比率超出了人们偶然期望的范围，考虑到团队的数量，因此不一定表明持续获得异常回报的可能性。这些发现与有效市场假说是一致的，强化了这样一种观念，即任何明显的优异表现都与统计噪声无法区分。此外，我们引入了一个程式化的竞争模型，以推导和分析为获得最高排名而优化的投资组合策略。结果表明，获得最高排名的任务并不一定等同于获得预期最佳投资回报的任务。通过构建一个偏离竞争对手策略的投资组合，即使没有获得异常回报的能力，也有可能提高自己获胜的机会。对提交的投资组合进行的实证分析表明，通过持有更高比例的空头头寸来区分自己与竞争对手的团队获得最高排名的可能性是其8倍以上，这与程式化模型的结果一致。

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引用次数: 0

All forecasters are not the same: Systematic patterns in predictive performance 所有的预测者都不一样：预测表现的系统模式

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-04-24 DOI: 10.1016/j.ijforecast.2025.02.008

Robert W. Rich , Joseph Tracy

Are all forecasters the same? Expectations models incorporating information rigidities typically imply that forecasters are interchangeable, which predicts an absence of systematic patterns in individual forecast behavior. Motivated by this prediction, we examine the European Central Bank’s Survey of Professional Forecasters and find, in contrast, that participants display systematic patterns in predictive performance both within and across target variables. Moreover, we document a new result from professional forecast surveys, which is that inter- and intra-forecaster relative predictive performance are strongly linked to the degree of difficulty in the forecasting environment. This insight can inform the ongoing development of expectations models.

所有的预测者都是一样的吗？包含信息刚性的期望模型通常意味着预测者是可互换的，这预示着个体预测行为中缺乏系统模式。在这一预测的推动下，我们研究了欧洲中央银行的专业预测者调查，发现相比之下，参与者在目标变量内部和跨目标变量的预测表现中都表现出系统的模式。此外，我们记录了专业预测调查的新结果，即预测者之间和内部的相对预测绩效与预测环境中的困难程度密切相关。这种洞察力可以为期望模型的持续开发提供信息。

引用次数: 0

VAR Model with Sparse Group LASSO for Multi-population Mortality Forecasting 多种群死亡率预测的稀疏群LASSO VAR模型

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-04-16 DOI: 10.1016/j.ijforecast.2025.03.004

Tim J. Boonen, Yuhuai Chen

We introduce a spatial–temporally weighted vector autoregressive (SWVAR) model for modeling and forecasting mortality rates across multiple populations. First, we stack the mortality rates of the populations and build a vector autoregressive (VAR) model. Next, we apply the sparse group least absolute shrinkage and selection operator (sparse group LASSO) for fitting to avoid overparameterization. Furthermore, we integrate spatial–temporal weights, derived from age differences and geographic centroid distances, into the grouped penalty term. These approaches allow the resulting model to effectively combine information from multiple populations and reduce confounding factors associated with combined modeling. We demonstrate through a series of empirical experiments that the spatial–temporally weighted VAR model enhances estimation accuracy and exhibits superior in-sample fitting and out-of-sample forecasting performance.

我们引入了一个时空加权向量自回归（SWVAR）模型，用于建模和预测多个人群的死亡率。首先，我们将种群的死亡率叠加，建立向量自回归（VAR）模型。其次，我们应用稀疏组最小绝对收缩算子和选择算子（稀疏组LASSO）进行拟合，以避免过度参数化。此外，我们将由年龄差异和地理质心距离得出的时空权重整合到分组惩罚项中。这些方法允许生成的模型有效地组合来自多个种群的信息，并减少与组合建模相关的混淆因素。我们通过一系列的实证实验证明，时空加权VAR模型提高了估计精度，并表现出优异的样本内拟合和样本外预测性能。

引用次数: 0

A survey of models and methods used for forecasting when investing in financial markets 对金融市场投资时用于预测的模型和方法的调查

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-04-11 DOI: 10.1016/j.ijforecast.2025.03.002

Kenwin Maung, Norman R. Swanson

The Makridakis M6 Financial Duathalon competition builds on prior M-competitions that focus on the properties of point and probabilistic forecasts of random variables by also evaluating investment decisions in financial markets. In particular, the M6 competition evaluates both forecasts and investment outcomes associated with the analysis of a large group of financial time series variables. Given the importance of return and risk forecasting when making investment decisions, a natural question in this context concerns what sorts of methods and models are available for said forecasting and were used by participants of the competition. In this survey, we discuss such methods and models, with a specific focus on the construction of financial time series forecasts using approaches designed for both discrete and continuous time setups and using both small and large (high dimensional and/or high frequency) datasets. Examples covered range from simple random walk-type models of returns to parametric GARCH and nonparametric integrated volatility methods for forecasting volatility (risk). We also present the results of a novel empirical illustration that underscores the difficulty in forecasting financial returns, even when using so-called big data.

Makridakis M6金融双人竞赛建立在先前的m竞赛的基础上，该竞赛通过评估金融市场中的投资决策来关注随机变量的点和概率预测的属性。特别是，M6竞赛评估与大量金融时间序列变量分析相关的预测和投资结果。考虑到在做出投资决策时回报和风险预测的重要性，在这种情况下，一个自然的问题是，哪种方法和模型可用于上述预测，并被竞赛参与者使用。在本调查中，我们讨论了这些方法和模型，特别关注金融时间序列预测的构建，使用为离散和连续时间设置设计的方法，并使用小型和大型（高维和/或高频）数据集。例子涵盖了从简单的随机游走型回报模型到参数GARCH和预测波动率（风险）的非参数综合波动率方法。我们还提出了一个新颖的实证说明的结果，强调了预测财务回报的困难，即使使用所谓的大数据。

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引用次数: 0

Predicting Australian federal electoral seats with machine learning 用机器学习预测澳大利亚联邦选举席位

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-03-21 DOI: 10.1016/j.ijforecast.2025.02.002

John ‘Jack’ Collins

I expand the international literature on election forecasting with the first application of machine learning (ML) in the Australian context. I apply these models to five elections from 2010 to 2022 and compare them with the dominant forecasting tool in Australia, the Mackerras pendulum. I evaluate these models’ accuracy in predicting the winning party for each electoral seat and estimating the total number of seats won by each party. Pendulum forecasts corrected with an extra trees model that incorporates state effects, seat-level unemployment rate, and vote share history predict up to 15 additional seats correctly six to three months before each election. The traditional pendulum is increasingly strained by polling errors and a larger crossbench. New modeling techniques will only emerge through experimentation. This study demonstrates the potential for ML-based election forecasting in Australia and provides a starting point for further efforts to surpass the pendulum.

我在澳大利亚的背景下首次应用机器学习（ML）扩展了关于选举预测的国际文献。我将这些模型应用于2010年至2022年的五次选举，并将它们与澳大利亚的主流预测工具——麦克拉斯钟摆（Mackerras pendulum）——进行比较。我评估了这些模型在预测每个选举席位的获胜政党和估计每个政党赢得的席位总数方面的准确性。钟摆预测采用额外树模型进行修正，该模型结合了州效应、席位水平失业率和投票份额历史，在每次选举前6到3个月准确预测了最多15个额外席位。传统的钟摆越来越受到民意调查错误和更大的交叉席位的影响。新的建模技术只有通过实验才能出现。这项研究证明了基于机器学习的选举预测在澳大利亚的潜力，并为进一步努力超越钟摆提供了一个起点。

引用次数: 0

Deep learning and NLP in cryptocurrency forecasting: Integrating financial, blockchain, and social media data 深度学习和NLP在加密货币预测中的应用：整合金融、区块链和社交媒体数据

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-03-18 DOI: 10.1016/j.ijforecast.2025.02.007

Vincent Gurgul , Stefan Lessmann , Wolfgang Karl Härdle

We introduce novel approaches to cryptocurrency price forecasting, leveraging Machine Learning (ML) and Natural Language Processing (NLP) techniques, with a focus on Bitcoin and Ethereum. By analysing news and social media content, primarily from Twitter and Reddit, we assess the impact of public sentiment on cryptocurrency markets. A distinctive feature of our methodology is the application of the BART MNLI zero-shot classification model to detect bullish and bearish trends, significantly advancing beyond traditional sentiment analysis. Additionally, we systematically compare a range of pre-trained and fine-tuned deep learning NLP models against conventional dictionary-based sentiment analysis methods. Another key contribution of our work is the adoption of local extrema alongside daily price movements as predictive targets, reducing trading frequency and portfolio volatility. Our findings demonstrate that integrating textual data into cryptocurrency price forecasting not only improves forecasting accuracy but also consistently enhances the profitability and Sharpe ratio across various validation scenarios, particularly when applying deep learning NLP techniques. The entire codebase of our experiments is available via an online repository: https://anonymous.4open.science/r/crypto-forecasting-public.

我们引入了新的加密货币价格预测方法，利用机器学习（ML）和自然语言处理（NLP）技术，重点关注比特币和以太坊。通过分析新闻和社交媒体内容（主要来自Twitter和Reddit），我们评估了公众情绪对加密货币市场的影响。我们的方法的一个显著特点是应用BART MNLI零射击分类模型来检测看涨和看跌趋势，大大超越了传统的情绪分析。此外，我们系统地比较了一系列预训练和微调的深度学习NLP模型与传统的基于字典的情感分析方法。我们工作的另一个关键贡献是采用局部极值和每日价格变动作为预测目标，减少交易频率和投资组合波动性。我们的研究结果表明，将文本数据集成到加密货币价格预测中不仅可以提高预测准确性，还可以在各种验证场景中持续提高盈利能力和夏普比率，特别是在应用深度学习NLP技术时。我们实验的整个代码库可通过在线存储库获得：https://anonymous.4open.science/r/crypto-forecasting-public。

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引用次数: 0

Disaggregating VIX 将波动率指数

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-03-18 DOI: 10.1016/j.ijforecast.2025.01.007

Stavros Degiannakis , Eleftheria Kafousaki

The present study highlights the economic profits of markets’ participants, accumulated from the disaggregated forecasts of the stock market’s implied volatility, generated from an ensemble modelling architecture. We incorporate six decomposition techniques, namely, the EMD, the EEMD, the SSA, the HVD, the EWT and the VMD and four different model frameworks that of AR, HAR, HW and LSTM, which are tested against a trading strategy. We diverge from quantifying forecast accuracy solely on statistical loss functions and report the cumulative returns of short or long exposure on roll adjusted VIX futures. The findings show that decomposing a time series into its intrinsic modes prior to modelling and forecasting, can result in generating forecast gains that are translated into improved profits for trading horizons of 1 to 22 days ahead. Important trading implications are drawn from these results.

目前的研究强调了市场参与者的经济利润，这些利润是通过对股票市场隐含波动率的分类预测积累起来的，这些预测是由一个集合建模架构生成的。我们采用了六种分解技术，即EMD、EEMD、SSA、HVD、EWT和VMD，以及四种不同的模型框架，即AR、HAR、HW和LSTM，并针对交易策略进行了测试。我们从量化预测准确性的统计损失函数和报告累计收益的短期或长期暴露在滚动调整波动率指数期货。研究结果表明，在建模和预测之前，将时间序列分解为其内在模式，可以产生预测收益，从而转化为未来1至22天交易周期的更高利润。从这些结果中可以得出重要的交易含义。

引用次数: 0

Election forecasting: Political economy models 选举预测：政治经济模型

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-03-18 DOI: 10.1016/j.ijforecast.2025.02.006

Michael S. Lewis-Beck , John Kenny , Debra Leiter , Andreas Erwin Murr , Onyinye B. Ogili , Mary Stegmaier , Charles Tien

We draw globally on a major election forecasting tool, political economy models. Vote intention polls in pre-election public surveys are a widely known approach; however, the lesser-known political economy models take a different scientific tack, relying on regression analysis and voting theory, particularly the force of “fundamentals.” We begin our discussion with two advanced industrial democracies, the US and UK. We then examine two less frequently forecasted cases, Mexico and Ghana, to highlight the potential for political-economic forecasting and the challenges faced. In evaluating the performance of political economy models, we argue for their accuracy but do not neglect lead time, parsimony, and transparency. Furthermore, we suggest how the political economic approach can be adapted to the changing landscape that democratic electorates face.

我们利用全球主要的选举预测工具——政治经济模型。选前民意调查中的投票意向调查是一种广为人知的方法；然而，鲜为人知的政治经济模型采取了不同的科学策略，依赖于回归分析和投票理论，特别是“基本面”的力量。我们从两个先进的工业民主国家——美国和英国——开始讨论。然后，我们研究了两个不太经常预测的案例，墨西哥和加纳，以突出政治经济预测的潜力和面临的挑战。在评估政治经济模型的表现时，我们主张它们的准确性，但不要忽视交货时间、节俭性和透明度。此外，我们建议如何使政治经济方法适应民主选民所面临的不断变化的环境。

引用次数: 0

Carpe diem: Can daily oil prices improve model-based forecasts of the real price of crude oil? 及时行乞：每日油价能否改善基于模型的原油实际价格预测？

IF 7.1 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2025-03-14 DOI: 10.1016/j.ijforecast.2025.02.009

Amor Aniss Benmoussa , Reinhard Ellwanger , Stephen Snudden

This paper proposes methods to include information from the underlying nominal daily series in model-based forecasts of average real series. We apply these methods to forecasts of the real price of crude oil. Models utilizing information from daily prices yield large forecast improvements and, in some cases, almost halve the forecast error compared to current specifications. We demonstrate for the first time that model-based forecasts of the real price of crude oil can outperform the traditional random walk forecast, that is, the end-of-month no-change forecast, at short forecast horizons.

本文提出了在基于模型的平均实数序列预测中包含基础名义日序列信息的方法。我们将这些方法应用于原油实际价格的预测。利用每日价格信息的模型产生了很大的预测改进，在某些情况下，与当前规范相比，预测误差几乎减少了一半。我们首次证明了基于模型的原油实际价格预测在短期预测范围内优于传统的随机漫步预测，即月末不变预测。

引用次数: 0

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数据库

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

期刊

International Journal of Forecasting

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