International Journal of Forecasting最新文献

英文中文

Econometric forecasting using ubiquitous news text: Text-enhanced factor model 使用无处不在的新闻文本的计量经济预测：文本增强因子模型

IF 6.9 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2024-12-05 DOI: 10.1016/j.ijforecast.2024.11.001

Beomseok Seo

News text is gaining increasing attention as a novel source for econometric forecasting. This paper revisits how narrative information is incorporated into econometric forecasting by effectively quantifying sector-specific textual information without requiring training data. We propose Theme Frequency Indices (TFIs), which utilize domain-specific subject-predicate patterns to measure public perception about the economy. TFIs for 15 sectors, including production, inflation, employment, capital investment, stock and house prices, and others, were examined and integrated into the Text-enhanced Factor Model (TFM), using latent factor structures. Empirical analysis based on over 18 million news articles from Korea reveals that TFM improves the accuracy of near-term GDP forecasts, demonstrating that simple text-mining techniques combined with domain knowledge can effectively leverage qualitative information in the news without costly training. The proposed method is applicable to a wide range of subjects for utilizing narrative information on the economy, offering a rapid and cost-effective approach.

新闻文本作为计量经济预测的一种新来源，正受到越来越多的关注。本文回顾了叙述性信息如何通过有效量化特定部门的文本信息而不需要训练数据纳入计量经济预测。我们提出主题频率指数（tfi），它利用特定领域的主谓模式来衡量公众对经济的看法。15个部门的tfi，包括生产、通货膨胀、就业、资本投资、股票和房价等，被检查和整合到文本增强因素模型（TFM），使用潜在因素结构。基于韩国1800多万篇新闻文章的实证分析表明，TFM提高了近期GDP预测的准确性，表明简单的文本挖掘技术与领域知识相结合，可以有效地利用新闻中的定性信息，而无需进行昂贵的培训。所建议的方法适用于广泛的主题，以利用关于经济的叙述性信息，提供了一种快速和具有成本效益的方法。

引用次数: 0

Testing for equal predictive accuracy with strong dependence 具有强依赖性的相等预测精度的测试

IF 6.9 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2024-12-05 DOI: 10.1016/j.ijforecast.2024.11.003

Laura Coroneo , Fabrizio Iacone

We analyse the properties of the Diebold and Mariano (1995) test in the presence of autocorrelation in the loss differential. We show that the power of the Diebold and Mariano (1995) test decreases as the dependence increases, making it more difficult to obtain statistically significant evidence of superior predictive ability against less accurate benchmarks. We also find that, after a certain threshold, the test has no power, and the correct null hypothesis is spuriously rejected. These results caution us to seriously consider the loss differential’s dependence properties before applying the Diebold and Mariano (1995) test.

我们分析了Diebold和Mariano（1995）在损失微分中存在自相关的情况下检验的性质。我们表明，Diebold和Mariano（1995）检验的效力随着依赖性的增加而降低，这使得在较不准确的基准上获得具有优越预测能力的统计显著证据变得更加困难。我们还发现，在某个阈值之后，检验没有效力，正确的零假设被虚假地拒绝。这些结果提醒我们在应用Diebold和Mariano（1995）检验之前要认真考虑损失微分的依赖性质。

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 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2024-11-30 DOI: 10.1016/j.ijforecast.2024.11.002

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

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.

M6预测竞赛是Makridakis竞赛序列中的第六个竞赛，重点是财务预测。M6竞赛的一个关键目标是通过研究市场参与者如何以及为什么做出投资决策，为围绕有效市场假说的辩论做出贡献。为了实现这些目标，M6竞赛调查了100种公开交易资产的预测准确性和投资表现。该竞赛采用了对多个时期真实数据的实时评估，参与者预测相对于其他资产的资产表现的横断面设置，以及对预测效用的直接评估。通过这种方式，我们能够衡量准确预测的好处，并在做出投资决策时评估预测的重要性。我们的研究结果强调了参与者在试图准确预测资产的相对表现时所面临的挑战，试图持续超越市场的巨大困难，提交的预测与投资决策之间的有限联系，信息交换和“群体智慧”的附加价值，以及在试图将预测与投资决策联系起来时利用风险模型的价值。

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引用次数: 0

Decision-focused linear pooling for probabilistic forecast combination 以决策为中心的概率预测组合线性池

IF 6.9 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2024-11-30 DOI: 10.1016/j.ijforecast.2024.11.006

Akylas Stratigakos , Salvador Pineda , Juan Miguel Morales

In real-world settings, decision-makers often have access to multiple forecasts for the same unknown quantity. Combining different forecasts has long been known to improve forecast quality, as measured by scoring rules in the case of probabilistic forecasting. However, improved forecast quality does not always translate into better decisions in a downstream problem that utilizes the resultant combined forecast as input. To this end, this work proposes a novel probabilistic forecast combination approach that accounts for the downstream stochastic optimization problem by which the decisions will be made. We propose a linear pool of probabilistic forecasts where the respective weights are learned by minimizing the expected decision cost of the induced combination, which we formulate as a nested optimization problem. Two methods are proposed for its solution: a gradient-based method that utilizes differential optimization layers, and a performance-based weighting method. The proposed decision-focused combination approach is validated in two integral problems associated with renewable energy integration in low-carbon power systems and compared against well-established combination methods. Namely, we examine an electricity market trading problem under stochastic solar production and a grid scheduling problem under stochastic wind production. The results illustrate that the proposed approach leads to lower expected downstream costs, while optimizing for forecast quality when estimating linear pool weights does not always translate into better decisions. Notably, optimizing for a combination of downstream cost and an accuracy-oriented scoring rule consistently leads to better decisions while also improving forecast quality.

在现实世界中，决策者通常可以获得对同一未知量的多种预测。结合不同的预测早已被认为可以提高预测质量，就像在概率预测的情况下通过评分规则来衡量的那样。然而，改进的预测质量并不总是转化为在下游问题中更好的决策，下游问题利用结果组合预测作为输入。为此，本工作提出了一种新的概率预测组合方法，该方法可以解释下游随机优化问题，从而做出决策。我们提出了一个线性概率预测池，其中通过最小化诱导组合的预期决策成本来学习各自的权重，我们将其表述为嵌套优化问题。针对该问题提出了两种解决方法：基于梯度的差分优化层法和基于性能的加权法。通过对低碳电力系统中可再生能源集成的两个整体问题进行验证，并与已有的组合方法进行比较。也就是说，我们研究了随机太阳能生产下的电力市场交易问题和随机风力生产下的电网调度问题。结果表明，所提出的方法导致较低的预期下游成本，而在估计线性池权重时优化预测质量并不总是转化为更好的决策。值得注意的是，对下游成本和以准确性为导向的评分规则的组合进行优化，可以在提高预测质量的同时做出更好的决策。

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引用次数: 0

Interpretable water level forecaster with spatiotemporal causal attention mechanisms 基于时空因果注意机制的可解释水位预报

IF 6.9 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2024-11-21 DOI: 10.1016/j.ijforecast.2024.10.003

Sungchul Hong , Yunjin Choi , Jong-June Jeon

Accurate forecasting of river water levels is vital for effectively managing traffic flow and mitigating the risks associated with natural disasters. This task presents challenges due to the intricate factors influencing the flow of a river. Recent advances in machine learning have introduced numerous effective forecasting methods. However, these methods lack interpretability due to their complex structure, resulting in limited reliability. Addressing this issue, this study proposes a deep learning model that quantifies interpretability, with an emphasis on water level forecasting. This model focuses on generating quantitative interpretability measurements, which align with the common knowledge embedded in the input data. This is facilitated by the utilization of a transformer architecture that is purposefully designed with masking, incorporating a multi-layer network that captures spatiotemporal causation. We perform a comparative analysis on the Han River dataset obtained from Seoul, South Korea, from 2016 to 2021. The results illustrate that our approach offers enhanced interpretability consistent with common knowledge, outperforming competing methods. The approach also enhances robustness against distribution shift.

准确预测河流水位对于有效管理交通流量和减轻与自然灾害有关的风险至关重要。由于影响河流流量的因素错综复杂，这项任务提出了挑战。机器学习的最新进展引入了许多有效的预测方法。但这些方法由于结构复杂，缺乏可解释性，可靠性有限。为了解决这个问题，本研究提出了一个深度学习模型，该模型量化了可解释性，重点是水位预测。该模型侧重于生成定量的可解释性度量，它与嵌入在输入数据中的公共知识相一致。这得益于变压器架构的利用，该架构有意设计了屏蔽，并结合了捕获时空因果关系的多层网络。我们对2016年至2021年从韩国首尔获得的汉江数据集进行了比较分析。结果表明，我们的方法提供了与常识一致的增强的可解释性，优于竞争方法。该方法还增强了对分布移位的鲁棒性。

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引用次数: 0

An overview of the effects of algorithm use on judgmental biases affecting forecasting 算法使用对影响预测的判断偏差的影响概述

IF 6.9 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2024-11-21 DOI: 10.1016/j.ijforecast.2024.09.007

Alvaro Chacon , Esther Kaufmann

In the realm of forecasting, judgmental biases often hinder efficiency and accuracy. Algorithms present a promising avenue for decision makers to enhance their forecasting performance. In this overview, we scrutinized the occurrence of the most relevant judgmental biases affecting forecasting across 162 papers, drawing from four recent reviews and papers published in forecasting journals, specifically focusing on the use of algorithms. Thirty-three of the 162 papers (20.4%) at least briefly mentioned one of twelve judgmental biases affecting forecasting. Our comprehensive analysis suggests that algorithms can potentially mitigate the adverse impacts of biases inherent in human judgment related to forecasting. Furthermore, these algorithms can leverage biases as an advantage, enhancing forecast accuracy. Intriguing revelations have surfaced, focusing mainly on four biases. By providing timely, relevant, well-performing, and consistent algorithmic advice, people can be effectively influenced to improve their forecasts, considering anchoring, availability, inconsistency, and confirmation bias. The findings highlight the gaps in the current research landscape and provide recommendations for practitioners. They also lay the groundwork for future studies on utilizing algorithms (e.g., large language models) and overcoming judgmental biases to improve forecasting performance.

在预测领域，判断偏差往往会阻碍效率和准确性。算法为决策者提供了一个有希望的途径来提高他们的预测性能。在这篇综述中，我们仔细审查了162篇论文中影响预测的最相关的判断偏差的发生，从最近的四篇评论和发表在预测期刊上的论文中提取，特别关注算法的使用。162篇论文中有33篇（20.4%）至少简要提到了影响预测的12种判断偏差中的一种。我们的综合分析表明，算法可以潜在地减轻与预测相关的人类判断中固有偏见的不利影响。此外，这些算法可以利用偏差作为一个优势，提高预测的准确性。有趣的爆料浮出水面，主要集中在四种偏见上。通过提供及时的、相关的、表现良好的和一致的算法建议，人们可以有效地影响他们的预测，考虑锚定、可用性、不一致性和确认偏差。研究结果突出了当前研究领域的差距，并为从业者提供了建议。他们还为未来利用算法（例如，大型语言模型）和克服判断偏差以提高预测性能的研究奠定了基础。

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引用次数: 0

Service-level anchoring in demand forecasting: The moderating impact of retail promotions and product perishability 需求预测中的服务水平锚定：零售促销和产品易腐性的调节影响

IF 6.9 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2024-11-18 DOI: 10.1016/j.ijforecast.2024.07.007

Ben Fahimnia , Tarkan Tan , Nail Tahirov

The development of demand plans involves the integration of demand forecasts, service-level prerequisites, replenishment constraints, and revenue projections. However, empirical evidence has brought to light that forecasters often fail to distinguish between demand forecasts and demand plans. More specifically, forecasters frequently incorporate service-level requirements into their demand forecasts, even when explicitly instructed not to do so. This study endeavors to investigate the potential moderating impacts of product perishability and the presence of sales promotions on this phenomenon. Our findings reveal that sales promotions can meaningfully moderate the influence of service levels, since individuals tend to exhibit an elevated propensity for overforecasting during promotional periods when service levels are high. Surprisingly, no compelling evidence is found for the moderating effect of product perishability.

需求计划的开发涉及需求预测、服务水平先决条件、补充约束和收入预测的集成。然而，经验证据表明，预测者往往无法区分需求预测和需求计划。更具体地说，预测人员经常将服务水平需求纳入他们的需求预测中，即使明确指示不要这样做。本研究旨在探讨产品易腐性和促销活动对这一现象的潜在调节作用。我们的研究结果表明，促销活动可以有效地调节服务水平的影响，因为在服务水平较高的促销期间，个人倾向于表现出更高的过度预测倾向。令人惊讶的是，没有令人信服的证据表明产品易腐性的调节作用。

引用次数: 0

Constructing hierarchical time series through clustering: Is there an optimal way for forecasting? 通过聚类构造分层时间序列：是否有最优的预测方法？

IF 6.9 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2024-11-13 DOI: 10.1016/j.ijforecast.2024.10.002

Bohan Zhang , Anastasios Panagiotelis , Han Li

Forecast reconciliation has attracted significant research interest in recent years, with most studies taking the hierarchy of time series as given. We extend existing work that uses time series clustering to construct hierarchies to improve forecast accuracy in three ways. First, we investigate multiple approaches to clustering, including different clustering algorithms, how time series are represented, and how the distance between time series is defined. We find that cluster-based hierarchies improve forecast accuracy relative to two-level hierarchies. Second, we devise an approach based on random permutation of hierarchies, keeping the hierarchy structure fixed while time series are randomly allocated to clusters. In doing so, we find that improvements in forecast accuracy that accrue from using clustering do not arise from grouping similar series but from the structure of the hierarchy. Third, we propose an approach based on averaging forecasts across hierarchies constructed using different clustering methods that is shown to outperform any single clustering method. All analysis is carried out on two benchmark datasets and a simulated dataset. Our findings provide new insights into the role of hierarchy construction in forecast reconciliation and offer valuable guidance on forecasting practice.

预测调和是近年来的研究热点，大多数研究都将时间序列的层次结构作为给定条件。我们扩展了现有的工作，使用时间序列聚类来构建层次结构，以三种方式提高预测精度。首先，我们研究了多种聚类方法，包括不同的聚类算法，如何表示时间序列，以及如何定义时间序列之间的距离。我们发现基于聚类的层次结构相对于两级层次结构提高了预测精度。其次，我们设计了一种基于层次结构随机排列的方法，在时间序列随机分配给聚类的同时保持层次结构的固定。在这样做的过程中，我们发现使用聚类所获得的预测精度的提高不是来自对相似序列的分组，而是来自层次结构。第三，我们提出了一种基于使用不同聚类方法构建的跨层次平均预测的方法，该方法被证明优于任何单一聚类方法。所有分析都是在两个基准数据集和一个模拟数据集上进行的。我们的研究结果为层次结构在预测调节中的作用提供了新的见解，并为预测实践提供了有价值的指导。

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引用次数: 0

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 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2024-11-04 DOI: 10.1016/j.ijforecast.2024.10.001

Spyros Makridakis , Evangelos Spiliotis , Maria Michailidis

The M6 competition aimed to identify methods that can accurately forecast asset returns and exploit such forecasts to make efficient investments. Specifically, the forecasting track of the competition required participants to estimate the probability that each of the 100 selected assets would be ranked within the first, second, third, fourth, or fifth quintile with regards to their relative percentage returns. Overall, less than 25% of the teams managed to estimate the probabilities more precisely than a benchmark that assumed equal probabilities for all quintiles. Moreover, those that did so reported inconsistent performance across the 12 submission points and minor forecast accuracy improvements. We identify price volatility as a key driver of forecast deterioration and show that avoiding overconfidence by assuming similar probabilities for symmetric quintiles can improve both forecast accuracy and portfolio efficiency. Interestingly, our findings hold true even when simple methods are employed to estimate the base predictions and investment weights.

M6竞赛旨在确定能够准确预测资产回报的方法，并利用这种预测进行有效投资。具体地说，比赛的预测轨迹要求参与者估计100个选定资产中的每一个在其相对百分比回报方面排名在第一、第二、第三、第四或第五个五分位数内的概率。总的来说，只有不到25%的团队能够比假设所有五分之一的概率相等的基准更精确地估计概率。此外，那些这样做的公司报告说，在12个提交点上的表现不一致，预测的准确性只有很小的提高。我们将价格波动确定为预测恶化的关键驱动因素，并表明通过假设对称五分位数的相似概率来避免过度自信可以提高预测准确性和投资组合效率。有趣的是，即使采用简单的方法来估计基本预测和投资权重，我们的发现也成立。

引用次数: 0

Robust recalibration of aggregate probability forecasts using meta-beliefs 利用元信念对总概率预测进行稳健的再校准

IF 6.9 2区经济学 Q1 ECONOMICS

International Journal of Forecasting

Pub Date : 2024-10-30 DOI: 10.1016/j.ijforecast.2024.09.005

Cem Peker , Tom Wilkening

Previous work suggests that aggregate probabilistic forecasts on a binary event are often conservative. Extremizing transformations that adjust the aggregate forecast away from the uninformed prior of 0.5 can improve calibration in many settings. However, such transformations may be problematic in decision problems where forecasters share a biased prior. In these problems, extremizing transformations can introduce further miscalibration. We develop a two-step algorithm where we first estimate the prior using each forecaster’s belief about the average forecast of others. We then transform away from this estimated prior in each forecasting problem. Our algorithm works in single-question forecasting problems and does not require past data. Evidence from experimental prediction tasks suggests that the resulting average probability forecast is robust to biased priors and improves calibration.

先前的研究表明，对二元事件的总体概率预测通常是保守的。在许多情况下，极值转换可以使总体预测远离0.5的未知先验，从而改善校准。然而，在预测者共享有偏见的先验的决策问题中，这种转换可能是有问题的。在这些问题中，极值变换会导致进一步的误标定。我们开发了一个两步算法，我们首先使用每个预测者对其他人平均预测的信念来估计先验。然后，我们在每个预测问题中都从这个估计先验中转换出来。我们的算法适用于单问题预测问题，不需要过去的数据。来自实验预测任务的证据表明，所得的平均概率预测对有偏先验具有鲁棒性，并改进了校准。

引用次数: 0

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