International Journal of Forecasting最新文献_第4页

Improving forecasts for heterogeneous time series by “averaging”, with application to food demand forecasts 用 "平均法 "改进异质时间序列的预测，并应用于粮食需求预测

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-03-14 DOI: 10.1016/j.ijforecast.2024.02.002

引用次数: 0

Dynamic prediction of the National Hockey League draft with rank-ordered logit models 利用秩序对数模型动态预测美国曲棍球联盟选秀情况

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-02-29 DOI: 10.1016/j.ijforecast.2024.02.003

引用次数: 0

The short-term predictability of returns in order book markets: A deep learning perspective 订单市场收益的短期可预测性：深度学习视角

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-02-27 DOI: 10.1016/j.ijforecast.2024.02.001

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引用次数: 0

Multivariate probabilistic CRPS learning with an application to day-ahead electricity prices 多元概率 CRPS 学习在日前电价中的应用

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-02-14 DOI: 10.1016/j.ijforecast.2024.01.005

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引用次数: 0

A Bayesian Dirichlet auto-regressive moving average model for forecasting lead times 用于预测提前期的贝叶斯 Dirichlet 自动回归移动平均模型

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-02-09 DOI: 10.1016/j.ijforecast.2024.01.004

{"title":"A Bayesian Dirichlet auto-regressive moving average model for forecasting lead times","authors":"","doi":"10.1016/j.ijforecast.2024.01.004","DOIUrl":"10.1016/j.ijforecast.2024.01.004","url":null,"abstract":"<div><p>In the hospitality industry, lead time data are a form of compositional data that are crucial for business planning, resource allocation, and staffing. Hospitality businesses accrue fees daily, but recognition of these fees is often deferred. This paper presents a novel class of Bayesian time series models, the Bayesian Dirichlet auto-regressive moving average (B-DARMA) model, designed specifically for compositional time series. The model is motivated by the analysis of five years of daily fees data from Airbnb, with the aim of forecasting the proportion of future fees that will be recognized in 12 consecutive monthly intervals. Each day’s compositional data are modeled as Dirichlet distributed, given the mean and a scale parameter. The mean is modeled using a vector auto-regressive moving average process, which depends on previous compositional data, previous compositional parameters, and daily covariates. The B-DARMA model provides a robust solution for analyzing large compositional vectors and time series of varying lengths. It offers efficiency gains through the choice of priors, yields interpretable parameters for inference, and produces reasonable forecasts. The paper also explores the use of normal and horseshoe priors for the vector auto-regressive and vector moving average coefficients, and for regression coefficients. The efficacy of the B-DARMA model is demonstrated through simulation studies and an analysis of Airbnb data.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1556-1567"},"PeriodicalIF":6.9,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207024000049/pdfft?md5=8f990fc55b20d6580d627b9419dc4176&pid=1-s2.0-S0169207024000049-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139918147","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

Forecasting UK inflation bottom up 自下而上预测英国通胀

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-02-05 DOI: 10.1016/j.ijforecast.2024.01.001

引用次数: 0

Network log-ARCH models for forecasting stock market volatility 预测股市波动的网络对数-ARCH 模型

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-01-25 DOI: 10.1016/j.ijforecast.2024.01.002

{"title":"Network log-ARCH models for forecasting stock market volatility","authors":"","doi":"10.1016/j.ijforecast.2024.01.002","DOIUrl":"10.1016/j.ijforecast.2024.01.002","url":null,"abstract":"<div><p>This paper presents a dynamic network autoregressive conditional heteroscedasticity (ARCH) model suitable for high-dimensional cases where multivariate ARCH models are typically no longer applicable. We adopt the theoretical foundations from spatiotemporal statistics and transfer the dynamic ARCH model processes to networks. The model integrates temporally lagged volatility and information from adjacent nodes, which may instantaneously spill across the entire network. The model is used to forecast volatility in the US stock market, and the edges are determined based on various distance and correlation measures between the time series. The performance of alternative network definitions is compared with independent univariate log-ARCH models in terms of out-of-sample prediction accuracy. The results indicate that more accurate forecasts are obtained with network-based models and that accuracy can be improved by combining the forecasts of different network definitions. We emphasise the significance for practitioners to integrate network structure information when developing volatility forecasts.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1539-1555"},"PeriodicalIF":6.9,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207024000025/pdfft?md5=784080169573790027c8d4ca4cbd201c&pid=1-s2.0-S0169207024000025-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139588033","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

Instance-based meta-learning for conditionally dependent univariate multi-step forecasting 基于实例的元学习，用于条件依赖型单变量多步骤预测

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-01-25 DOI: 10.1016/j.ijforecast.2023.12.010

{"title":"Instance-based meta-learning for conditionally dependent univariate multi-step forecasting","authors":"","doi":"10.1016/j.ijforecast.2023.12.010","DOIUrl":"10.1016/j.ijforecast.2023.12.010","url":null,"abstract":"<div><p><span>Multi-step prediction is a key challenge in univariate forecasting. However, forecasting accuracy<span> decreases as predictions are made further into the future. This is caused by the decreasing predictability and the error propagation along the horizon. In this paper, we propose a novel method called </span></span><span>Forecasted Trajectory Neighbors</span> (<span>FTN</span><span>) for multi-step forecasting with univariate time series. </span><span>FTN</span><span> is a meta-learning strategy that can be integrated with any state-of-the-art multi-step forecasting approach. It works by using training observations to correct the errors made during multiple predictions. This is accomplished by retrieving the nearest neighbors of the multi-step forecasts and averaging these for prediction. The motivation is to introduce, in a lightweight manner, a conditional dependent constraint across the forecasting horizons. Such a constraint, not always taken into account by most strategies, can be considered as a sort of regularization<span> element. We carried out extensive experiments using 7795 time series from different application domains. We found that our method improves the performance of several state-of-the-art multi-step forecasting methods. An implementation of the proposed method is publicly available online, and the experiments are reproducible.</span></span></p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 4","pages":"Pages 1507-1520"},"PeriodicalIF":6.9,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139587447","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

Survey density forecast comparison in small samples 小样本调查密度预测比较

IF 6.9 2区经济学

International Journal of Forecasting Pub Date : 2024-01-25 DOI: 10.1016/j.ijforecast.2023.12.007

引用次数: 0

Crowd prediction systems: Markets, polls, and elite forecasters 人群预测系统：市场、民意调查和精英预测者

IF 7.9 2区经济学

International Journal of Forecasting Pub Date : 2024-01-22 DOI: 10.1016/j.ijforecast.2023.12.009

Pavel Atanasov, Jens Witkowski, Barbara Mellers, Philip Tetlock

{"title":"Crowd prediction systems: Markets, polls, and elite forecasters","authors":"Pavel Atanasov, Jens Witkowski, Barbara Mellers, Philip Tetlock","doi":"10.1016/j.ijforecast.2023.12.009","DOIUrl":"https://doi.org/10.1016/j.ijforecast.2023.12.009","url":null,"abstract":"<p>What systems should we use to elicit and aggregate judgmental forecasts? Who should be asked to make such forecasts? We address these questions by assessing two widely used crowd prediction systems: prediction markets and prediction polls. Our main test compares a prediction market against team-based prediction polls, using data from a large, multi-year forecasting competition. Each of these two systems uses inputs from either a large, sub-elite or a small, elite crowd. We find that small, elite crowds outperform larger ones, whereas the two systems are statistically tied. In addition to this main research question, we examine two complementary questions. First, we compare two market structures—continuous double auction (CDA) markets and logarithmic market scoring rule (LMSR) markets—and find that the LMSR market produces more accurate forecasts than the CDA market, especially on low-activity questions. Second, given the importance of elite forecasters, we compare the talent-spotting properties of the two systems and find that markets and polls are equally effective at identifying elite forecasters. Overall, the performance benefits of “superforecasting” hold across systems. Managers should move towards identifying and deploying small, select crowds to maximize forecasting performance.</p>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"36 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139587673","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