International Journal of Forecasting最新文献

{"title":"Forecasting adversarial actions using judgment decomposition-recomposition","authors":"Yolanda Gomez ,&nbsp;Jesus Rios ,&nbsp;David Rios Insua ,&nbsp;Jose Vila","doi":"10.1016/j.ijforecast.2024.03.004","DOIUrl":"10.1016/j.ijforecast.2024.03.004","url":null,"abstract":"<div><div><span>In domains such as homeland security, cybersecurity, and competitive marketing, it is frequently the case that analysts need to forecast actions by other intelligent agents that impact the problem of interest. Standard structured expert judgment elicitation techniques may fall short in this type of problem as they do not explicitly take into account intentionality. We present a decomposition technique based on adversarial </span>risk analysis<span> followed by a behavioural recomposition using discrete choice models that facilitate such elicitation process and illustrate its reasonable performance through behavioural experiments.</span></div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 1","pages":"Pages 76-91"},"PeriodicalIF":6.9,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705040","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}

{"title":"Conditionally optimal weights and forward-looking approaches to combining forecasts","authors":"Christopher G. Gibbs,&nbsp;Andrey L. Vasnev","doi":"10.1016/j.ijforecast.2024.03.002","DOIUrl":"10.1016/j.ijforecast.2024.03.002","url":null,"abstract":"<div><p>In forecasting, there is a tradeoff between in-sample fit and out-of-sample forecast accuracy. Parsimonious model specifications typically outperform richer model specifications. Consequently, information is often withheld from a forecast to prevent over-fitting the data. We show that one way to exploit this information is through forecast combination. Optimal combination weights in this environment minimize the conditional mean squared error that balances the conditional bias and the conditional variance of the combination. The bias-adjusted conditionally optimal forecast weights are time varying and forward looking. Real-time tests of conditionally optimal combinations of model-based forecasts and surveys of professional forecasters show significant gains in forecast accuracy relative to standard benchmarks for inflation and other macroeconomic variables.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1734-1751"},"PeriodicalIF":6.9,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S016920702400027X/pdfft?md5=a9a2fe1ffd1994772550f8bd88d4ead2&pid=1-s2.0-S016920702400027X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142098323","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}

{"title":"Measuring probabilistic coherence to identify superior forecasters","authors":"Emily H. Ho ,&nbsp;David V. Budescu ,&nbsp;Mark Himmelstein","doi":"10.1016/j.ijforecast.2024.02.005","DOIUrl":"10.1016/j.ijforecast.2024.02.005","url":null,"abstract":"<div><div>Forecasts, or subjective probability assessments of uncertain events, are characterized by two qualities: coherence, the degree to which the judgments are internally consistent, and correspondence, the extent to which judgments are accurate. Recent evidence suggests that more coherent forecasts tend to be more accurate. However, currently, there is no good stand-alone measure of probabilistic coherence. We developed and validated the Coherence Forecasting Scale (CFS). This questionnaire assesses how well people understand and apply probabilistic reasoning rules such as relations between joint and disjoint probabilities, probability complementarity, stochastic dominance, and monotonicity. In three incentivized forecasting tournaments, including one from an online public forecasting platform, judges who scored higher on the CFS were also more accurate. Notably, across all tournaments, the CFS dominates all administered individual difference and demographic measures in explanatory power predicting judgment accuracy, providing empirical evidence that coherence and accuracy are strongly linked.</div></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"41 2","pages":"Pages 596-612"},"PeriodicalIF":6.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140402571","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}

{"title":"A loss discounting framework for model averaging and selection in time series models","authors":"Dawid Bernaciak,&nbsp;Jim E. Griffin","doi":"10.1016/j.ijforecast.2024.03.001","DOIUrl":"10.1016/j.ijforecast.2024.03.001","url":null,"abstract":"<div><p>We introduce a loss discounting framework for model and forecast combination, which generalises and combines Bayesian model synthesis and generalized Bayes methodologies. We use a loss function to score the performance of different models and introduce a multilevel discounting scheme that allows for a flexible specification of the dynamics of the model weights. This novel and simple model combination approach can be easily applied to large-scale model averaging/selection, handle unusual features such as sudden regime changes and be tailored to different forecasting problems. We compare our method to established and state-of-the-art methods for several macroeconomic forecasting examples. The proposed method offers an attractive, computationally efficient alternative to the benchmark methodologies and often outperforms more complex techniques.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1721-1733"},"PeriodicalIF":6.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207024000268/pdfft?md5=e82af80360f437576bd13d36da34ea30&pid=1-s2.0-S0169207024000268-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142098322","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}

{"title":"Hierarchical forecasting at scale","authors":"Olivier Sprangers ,&nbsp;Wander Wadman ,&nbsp;Sebastian Schelter ,&nbsp;Maarten de Rijke","doi":"10.1016/j.ijforecast.2024.02.006","DOIUrl":"10.1016/j.ijforecast.2024.02.006","url":null,"abstract":"<div><p>Hierarchical forecasting techniques allow for the creation of forecasts that are coherent with respect to a pre-specified hierarchy of the underlying time series. This targets a key problem in e-commerce, where we often find millions of products across many product hierarchies, and forecasts must be made for individual products and product aggregations. However, existing hierarchical forecasting techniques scale poorly when the number of time series increases, which limits their applicability at a scale of millions of products.</p><p>In this paper, we propose to learn a coherent forecast for millions of products with a single bottom-level forecast model by using a loss function that directly optimizes the hierarchical product structure. We implement our loss function using sparse linear algebra, such that the number of operations in our loss function scales quadratically rather than cubically with the number of products and levels in the hierarchical structure. The benefit of our sparse hierarchical loss function is that it provides practitioners with a method of producing bottom-level forecasts that are coherent to any chosen cross-sectional or temporal hierarchy. In addition, removing the need for a post-processing step as required in traditional hierarchical forecasting techniques reduces the computational cost of the prediction phase in the forecasting pipeline and its deployment complexity.</p><p>In our tests on the public M5 dataset, our sparse hierarchical loss function performs up to 10% better as measured by RMSE and MAE than the baseline loss function. Next, we implement our sparse hierarchical loss function within a gradient boosting-based forecasting model at bol.com, a large European e-commerce platform. At bol.com, each day, a forecast for the weekly demand of every product for the next twelve weeks is required. In this setting, our sparse hierarchical loss resulted in an improved forecasting performance as measured by RMSE of about 2% at the product level, compared to the baseline model, and an improvement of about 10% at the product level as measured by MAE. Finally, we found an increase in forecasting performance of about 5%–10% (both RMSE and MAE) when evaluating the forecasting performance across the cross-sectional hierarchies we defined. These results demonstrate the usefulness of our sparse hierarchical loss applied to a production forecasting system at a major e-commerce platform.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1689-1700"},"PeriodicalIF":6.9,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207024000116/pdfft?md5=9360c19bcfcd3805bc4065518de97167&pid=1-s2.0-S0169207024000116-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142098320","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}

{"title":"Factor-augmented forecasting in big data","authors":"","doi":"10.1016/j.ijforecast.2024.02.004","DOIUrl":"10.1016/j.ijforecast.2024.02.004","url":null,"abstract":"<div><p>This paper evaluates the predictive performance of various factor estimation methods in big data. Extensive forecasting experiments are examined using seven factor estimation methods with 13 decision rules determining the number of factors. The out-of-sample forecasting results show that the first Partial Least Squares factor (1-PLS) tends to be the best-performing method among all the possible alternatives. This finding is prevalent in many target variables under different forecasting horizons and models. This significant improvement can be explained by the PLS factor estimation strategy that considers the covariance with the target variable. Second, using a consistently estimated number of factors may not necessarily improve forecasting performance. The greatest predictive gain often derives from decision rules that do not consistently estimate the true number of factors.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1660-1688"},"PeriodicalIF":6.9,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207024000098/pdfft?md5=bc3f0812065997cb8e01528b63ed0435&pid=1-s2.0-S0169207024000098-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140151749","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}

{"title":"Improving forecasts for heterogeneous time series by “averaging”, with application to food demand forecasts","authors":"","doi":"10.1016/j.ijforecast.2024.02.002","DOIUrl":"10.1016/j.ijforecast.2024.02.002","url":null,"abstract":"<div><p>A common forecasting setting in real-world applications considers a set of possibly heterogeneous time series of the same domain. Due to the different properties of each time series, such as length, obtaining forecasts for each individual time series in a straightforward way is challenging. This paper proposes a general framework utilizing a similarity measure in dynamic time warping to find similar time series to build neighborhoods in a <span><math><mi>k</mi></math></span>-nearest neighbor fashion and improve forecasts of possibly simple models by averaging. Several ways of performing the averaging are suggested, and theoretical arguments underline the usefulness of averaging for forecasting. Additionally, diagnostic tools are proposed for a deep understanding of the procedure.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1622-1645"},"PeriodicalIF":6.9,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207024000074/pdfft?md5=9d271047270035c3248727ce86799d68&pid=1-s2.0-S0169207024000074-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140150894","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}

{"title":"Dynamic prediction of the National Hockey League draft with rank-ordered logit models","authors":"","doi":"10.1016/j.ijforecast.2024.02.003","DOIUrl":"10.1016/j.ijforecast.2024.02.003","url":null,"abstract":"<div><p>The National Hockey League (NHL) Entry Draft has been an active area of research in hockey analytics over the past decade. Prior research has explored predictive modelling for draft results using player information and statistics as well as ranking data from draft experts. In this paper, we develop a new modelling framework for this problem using a Bayesian rank-ordered logit model based on draft ranking data from industry experts between 2019 and 2022. This model builds upon previous approaches by incorporating team tendencies, addressing within-ranking dependence between players, and solving various other challenges of working with rank-ordered outcomes, such as incorporating both unranked players and rankings that only consider a subset of the available pool of players (e.g., North American skaters, European goalies, etc.).</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1646-1659"},"PeriodicalIF":6.9,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140033580","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}

{"title":"The short-term predictability of returns in order book markets: A deep learning perspective","authors":"","doi":"10.1016/j.ijforecast.2024.02.001","DOIUrl":"10.1016/j.ijforecast.2024.02.001","url":null,"abstract":"<div><p>This paper uses deep learning techniques to conduct a systematic large-scale analysis of order book-driven predictability in high-frequency returns. First, we introduce a new and robust representation of the order book, the volume representation. Next, we conduct an extensive empirical experiment to address various questions regarding predictability. We investigate if and how far ahead there is predictability, the importance of a robust data representation, the advantages of multi-horizon modeling, and the presence of universal trading patterns. We use model confidence sets, which provide a formalized statistical inference framework well suited to answer these questions. Our findings show that at high frequencies, predictability in mid-price returns is not just present but ubiquitous. The performance of the deep learning models is strongly dependent on the choice of order book representation, and in this respect, the volume representation appears to have multiple practical advantages.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1587-1621"},"PeriodicalIF":6.9,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207024000062/pdfft?md5=934c9d6083122876446de96b50f868e6&pid=1-s2.0-S0169207024000062-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140034212","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}

{"title":"Multivariate probabilistic CRPS learning with an application to day-ahead electricity prices","authors":"","doi":"10.1016/j.ijforecast.2024.01.005","DOIUrl":"10.1016/j.ijforecast.2024.01.005","url":null,"abstract":"<div><p>This paper presents a new method for combining (or aggregating or ensembling) multivariate probabilistic forecasts, considering dependencies between quantiles and marginals through a smoothing procedure that allows for online learning. We discuss two smoothing methods: dimensionality reduction using Basis matrices and penalized smoothing. The new online learning algorithm generalizes the standard CRPS learning framework into multivariate dimensions. It is based on Bernstein Online Aggregation (BOA) and yields optimal asymptotic learning properties. The procedure uses horizontal aggregation, i.e., aggregation across quantiles. We provide an in-depth discussion on possible extensions of the algorithm and several nested cases related to the existing literature on online forecast combination. We apply the proposed methodology to forecasting day-ahead electricity prices, which are 24-dimensional distributional forecasts. The proposed method yields significant improvements over uniform combination in terms of continuous ranked probability score (CRPS). We discuss the temporal evolution of the weights and hyperparameters and present the results of reduced versions of the preferred model. A fast C＋＋implementation of the proposed algorithm is provided in the open-source <span>R</span>-Package <em>profoc</em> on CRAN.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1568-1586"},"PeriodicalIF":6.9,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207024000050/pdfft?md5=90383c67e47ea688069f29a0e9023fff&pid=1-s2.0-S0169207024000050-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139918163","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}