International Journal of Forecasting最新文献_第5页

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

International Journal of Forecasting Pub Date : 2024-12-05 DOI: 10.1016/j.ijforecast.2024.11.001

Beomseok Seo

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

International Journal of Forecasting Pub Date : 2024-12-05 DOI: 10.1016/j.ijforecast.2024.11.003

Laura Coroneo , Fabrizio Iacone

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 : 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":"2024-11-30","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

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

International Journal of Forecasting Pub Date : 2024-11-30 DOI: 10.1016/j.ijforecast.2024.11.006

Akylas Stratigakos , Salvador Pineda , Juan Miguel Morales

{"title":"Decision-focused linear pooling for probabilistic forecast combination","authors":"Akylas Stratigakos , Salvador Pineda , Juan Miguel Morales","doi":"10.1016/j.ijforecast.2024.11.006","DOIUrl":"10.1016/j.ijforecast.2024.11.006","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1112-1125"},"PeriodicalIF":6.9,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144211935","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

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

International Journal of Forecasting Pub Date : 2024-11-21 DOI: 10.1016/j.ijforecast.2024.10.003

Sungchul Hong , Yunjin Choi , Jong-June Jeon

{"title":"Interpretable water level forecaster with spatiotemporal causal attention mechanisms","authors":"Sungchul Hong , Yunjin Choi , Jong-June Jeon","doi":"10.1016/j.ijforecast.2024.10.003","DOIUrl":"10.1016/j.ijforecast.2024.10.003","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1037-1054"},"PeriodicalIF":6.9,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212021","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

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

International Journal of Forecasting Pub Date : 2024-11-21 DOI: 10.1016/j.ijforecast.2024.09.007

Alvaro Chacon , Esther Kaufmann

{"title":"An overview of the effects of algorithm use on judgmental biases affecting forecasting","authors":"Alvaro Chacon , Esther Kaufmann","doi":"10.1016/j.ijforecast.2024.09.007","DOIUrl":"10.1016/j.ijforecast.2024.09.007","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 424-439"},"PeriodicalIF":6.9,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

International Journal of Forecasting Pub Date : 2024-11-18 DOI: 10.1016/j.ijforecast.2024.07.007

Ben Fahimnia , Tarkan Tan , Nail Tahirov

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

International Journal of Forecasting Pub Date : 2024-11-13 DOI: 10.1016/j.ijforecast.2024.10.002

Bohan Zhang , Anastasios Panagiotelis , Han Li

{"title":"Constructing hierarchical time series through clustering: Is there an optimal way for forecasting?","authors":"Bohan Zhang , Anastasios Panagiotelis , Han Li","doi":"10.1016/j.ijforecast.2024.10.002","DOIUrl":"10.1016/j.ijforecast.2024.10.002","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 3","pages":"Pages 1022-1036"},"PeriodicalIF":6.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212011","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

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

Spyros Makridakis , Evangelos Spiliotis , Maria Michailidis

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

International Journal of Forecasting Pub Date : 2024-10-30 DOI: 10.1016/j.ijforecast.2024.09.005

Cem Peker , Tom Wilkening

引用次数: 0