Daniele Girolimetto , George Athanasopoulos , Tommaso Di Fonzo , Rob J. Hyndman
{"title":"Cross-temporal probabilistic forecast reconciliation: Methodological and practical issues","authors":"Daniele Girolimetto , George Athanasopoulos , Tommaso Di Fonzo , Rob J. Hyndman","doi":"10.1016/j.ijforecast.2023.10.003","DOIUrl":"10.1016/j.ijforecast.2023.10.003","url":null,"abstract":"<div><p>Forecast reconciliation is a post-forecasting process that involves transforming a set of incoherent forecasts into coherent forecasts which satisfy a given set of linear constraints for a multivariate time series. In this paper, we extend the current state-of-the-art cross-sectional probabilistic forecast reconciliation approach to encompass a cross-temporal framework, where temporal constraints are also applied. Our proposed methodology employs both parametric Gaussian and non-parametric bootstrap approaches to draw samples from an incoherent cross-temporal distribution. To improve the estimation of the forecast error covariance matrix, we propose using multi-step residuals, especially in the time dimension where the usual one-step residuals fail. To address high-dimensionality issues, we present four alternatives for the covariance matrix, where we exploit the two-fold nature (cross-sectional and temporal) of the cross-temporal structure, and introduce the idea of overlapping residuals. We assess the effectiveness of the proposed cross-temporal reconciliation approaches through a simulation study that investigates their theoretical and empirical properties and two forecasting experiments, using the Australian GDP and the Australian Tourism Demand datasets. For both applications, the optimal cross-temporal reconciliation approaches significantly outperform the incoherent base forecasts in terms of the continuous ranked probability score and the energy score. Overall, the results highlight the potential of the proposed methods to improve the accuracy of probabilistic forecasts and to address the challenge of integrating disparate scenarios while coherently taking into account short-term operational, medium-term tactical, and long-term strategic planning.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 3","pages":"Pages 1134-1151"},"PeriodicalIF":7.9,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207023001024/pdfft?md5=b9978f6e8d4d5d5d9fff37d9d9c92f92&pid=1-s2.0-S0169207023001024-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135509503","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}
Timo Dimitriadis , Tilmann Gneiting , Alexander I. Jordan , Peter Vogel
{"title":"Evaluating probabilistic classifiers: The triptych","authors":"Timo Dimitriadis , Tilmann Gneiting , Alexander I. Jordan , Peter Vogel","doi":"10.1016/j.ijforecast.2023.09.007","DOIUrl":"10.1016/j.ijforecast.2023.09.007","url":null,"abstract":"<div><p>Probability forecasts for binary outcomes, often referred to as probabilistic classifiers or confidence scores, are ubiquitous in science and society, and methods for evaluating and comparing them are in great demand. We propose and study a triptych of diagnostic graphics focusing on distinct and complementary aspects of forecast performance: Reliability curves address calibration, receiver operating characteristic (ROC) curves diagnose discrimination ability, and Murphy curves visualize overall predictive performance and value. A Murphy curve shows a forecast’s mean elementary scores, including the widely used misclassification rate, and the area under a Murphy curve equals the mean Brier score. For a calibrated forecast, the reliability curve lies on the diagonal, and for competing calibrated forecasts, the ROC and Murphy curves share the same number of crossing points. We invoke the recently developed CORP (Consistent, Optimally binned, Reproducible, and Pool-Adjacent-Violators (PAV) algorithm-based) approach to craft reliability curves and decompose a mean score into miscalibration (MCB), discrimination (DSC), and uncertainty (UNC) components. Plots of the <span><math><mtext>DSC</mtext></math></span> measure of discrimination ability versus the calibration metric <span><math><mtext>MCB</mtext></math></span> visualize classifier performance across multiple competitors. The proposed tools are illustrated in empirical examples from astrophysics, economics, and social science.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 3","pages":"Pages 1101-1122"},"PeriodicalIF":7.9,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207023000997/pdfft?md5=bd26faa9dd0165399770a39be8802f6a&pid=1-s2.0-S0169207023000997-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455588","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":"Short-term stock price trend prediction with imaging high frequency limit order book data","authors":"Wuyi Ye, Jinting Yang, Pengzhan Chen","doi":"10.1016/j.ijforecast.2023.10.008","DOIUrl":"10.1016/j.ijforecast.2023.10.008","url":null,"abstract":"<div><p>Predicting price movements over a short period is a challenging problem in high-frequency trading. Deep learning methods have recently been used to forecast short-term prices via limit order book (LOB) data. In this paper, we propose a framework to convert LOB data into a series of standard images in 2D matrices and predict the mid-price movements via an image-based convolutional neural network (CNN). The empirical study shows that the image-based CNN model outperforms other traditional machine learning and deep learning methods based on raw LOB data. Our findings suggest that the additional information implicit in LOB images contributes to short-term price forecasting.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 3","pages":"Pages 1189-1205"},"PeriodicalIF":7.9,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135410354","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":"DeepTVAR: Deep learning for a time-varying VAR model with extension to integrated VAR","authors":"Xixi Li, Jingsong Yuan","doi":"10.1016/j.ijforecast.2023.10.001","DOIUrl":"10.1016/j.ijforecast.2023.10.001","url":null,"abstract":"<div><p>This paper proposes a new approach called DeepTVAR that employs a deep learning methodology for vector autoregressive (VAR) modeling and prediction with time-varying parameters. By optimizing the VAR parameters with a long short-term memory (LSTM) network, we retain the Markovian dependence for prediction purposes and make full use of the recurrent structure and powerful learning ability of the LSTM. To ensure the stability of the model, we enforce the causality condition on the autoregressive coefficients using the Ansley–Kohn transform. We provide a simulation study of the estimation ability using realistic curves generated from data. The model is extended to integrated VAR with time-varying parameters, and we compare its forecasting performance with existing methods when applied to energy price data.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 3","pages":"Pages 1123-1133"},"PeriodicalIF":7.9,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207023001000/pdfft?md5=bc81dadfc6183648fd77733111eafc20&pid=1-s2.0-S0169207023001000-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136153899","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":"Out-of-sample predictability in predictive regressions with many predictor candidates","authors":"Jesús Gonzalo , Jean-Yves Pitarakis","doi":"10.1016/j.ijforecast.2023.10.005","DOIUrl":"10.1016/j.ijforecast.2023.10.005","url":null,"abstract":"<div><p>This paper is concerned with detecting the presence of out-of-sample predictability in linear predictive regressions with a potentially large set of candidate predictors. We propose a procedure based on out-of-sample MSE comparisons that is implemented in a pairwise manner using one predictor at a time. This results in an aggregate test statistic that is standard normally distributed under the global null hypothesis of no linear predictability. Predictors can be highly persistent, purely stationary, or a combination of both. Upon rejecting the null hypothesis, we introduce a predictor screening procedure designed to identify the most active predictors. An empirical application to key predictors of US economic activity illustrates the usefulness of our methods. It highlights the important forward-looking role played by the series of manufacturing new orders.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 3","pages":"Pages 1166-1178"},"PeriodicalIF":7.9,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207023001048/pdfft?md5=80ff4bc94530f3c1aff904ea06341ce6&pid=1-s2.0-S0169207023001048-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136119733","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}
Jennifer L. Castle , Jurgen A. Doornik , David F. Hendry
{"title":"Improving models and forecasts after equilibrium-mean shifts","authors":"Jennifer L. Castle , Jurgen A. Doornik , David F. Hendry","doi":"10.1016/j.ijforecast.2023.09.006","DOIUrl":"10.1016/j.ijforecast.2023.09.006","url":null,"abstract":"<div><p>Equilibrium-mean shifts can result from changes in intercepts with constant dynamics, or be induced by shifts in dynamics with non-zero data means, or both. Induced shifts distort parameter estimates and create a discrepancy between the forecast origin and the equilibrium mean, leading to forecast failure and requiring modifications to previous forecast-error taxonomies. Step-indicator saturation can detect induced shifts, but that does not correct forecast failure. To discriminate direct from induced equilibrium-mean shifts, we augment the model by multiplicative indicators where all selected step indicators interact with the lagged regressand. Forecasts can be markedly improved after induced shifts by including these interactive indicators.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 3","pages":"Pages 1085-1100"},"PeriodicalIF":7.9,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169207023000985/pdfft?md5=b5c8b46641e835f9af3feb49dfa1d5b3&pid=1-s2.0-S0169207023000985-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136118139","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}
Luciano Vereda , João Savignon , Tarciso Gouveia da Silva
{"title":"A theory-based method to evaluate the impact of central bank inflation forecasts on private inflation expectations","authors":"Luciano Vereda , João Savignon , Tarciso Gouveia da Silva","doi":"10.1016/j.ijforecast.2023.09.005","DOIUrl":"10.1016/j.ijforecast.2023.09.005","url":null,"abstract":"<div><p><span><span>We propose a theory-based method to assess the impact of central banks’ inflation forecasts on private </span>inflation expectations. We use regressions derived from a leader-follower model with noisy information and public signals. The leader is the Central Bank (CB), which solves a signal extraction problem to estimate the rational expectation of inflation. Private agents then act by solving an analogous problem to estimate this same value by using their own information and the forecasts disclosed by the CB. The method allows for estimating the structural parameters that characterize noisy information models, which are hard to estimate using purely </span>econometric tools. It also sheds light on the issue of the alleged CB’s superiority in predicting inflation behavior.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 3","pages":"Pages 1069-1084"},"PeriodicalIF":7.9,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135706061","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}
Florian Huber , Luca Onorante , Michael Pfarrhofer
{"title":"Forecasting euro area inflation using a huge panel of survey expectations","authors":"Florian Huber , Luca Onorante , Michael Pfarrhofer","doi":"10.1016/j.ijforecast.2023.09.003","DOIUrl":"10.1016/j.ijforecast.2023.09.003","url":null,"abstract":"<div><p>In this paper, we forecast euro area inflation and its main components using a massive number of time series on survey expectations obtained from the European Commission’s Business and Consumer Survey. To make the estimation of such a huge model tractable, we use recent advances in computational statistics to carry out posterior simulation and inference. Our findings suggest that including a wide range of firms’ and consumers’ opinions about future economic developments offers useful information to forecast prices and assess tail risks to inflation. These predictive improvements arise from surveys related to expected inflation and other questions related to the general economic environment. Finally, we find that firms’ expectations about the future seem to have more predictive content than consumer expectations.</p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 3","pages":"Pages 1042-1054"},"PeriodicalIF":7.9,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S016920702300095X/pdfft?md5=f19e1ad69aaed5d77ad8b676ed1d5090&pid=1-s2.0-S016920702300095X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135607152","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}
Juan R. Trapero, Enrique Holgado de Frutos, Diego J. Pedregal
{"title":"Demand forecasting under lost sales stock policies","authors":"Juan R. Trapero, Enrique Holgado de Frutos, Diego J. Pedregal","doi":"10.1016/j.ijforecast.2023.09.004","DOIUrl":"10.1016/j.ijforecast.2023.09.004","url":null,"abstract":"<div><p>Demand forecasting is a crucial task within supply chain management. Stock control policies are directly affected by the precision of probabilistic demand forecasts. For instance, safety stocks and reorder points are based on those forecasts. However, forecasting and replenishment policies have typically been studied separately. In this work, we explore the influence of inventory assumptions on the selection of the forecasting model<span>. In particular, we consider when the stock policy follows a lost sales context and the demand is estimated by means of sales data. In that case, forecasting models should use censored demand estimations. Unfortunately, the literature about censored demand forecasting remains very limited, without an accepted general solution for this problem. In this work, we bridge that gap by proposing the Tobit Kalman filter (TKF). To the best of our knowledge, this is the first time that the TKF has been applied to supply chain demand forecasting, and this approach may represent a general solution for lost sales contexts. The TKF is compared with a previous ad hoc censored demand forecasting solution that is based on single exponential smoothing. In addition, we show the performance of the TKF when dealing with trends where ad hoc approaches are not available for use as benchmarks. To express the potential benefits of the proposed approach in terms of costs and the service level, a newsvendor stock policy is employed. Simulated demand data and a case study are used to illustrate the significant advantages of the proposed tool.</span></p></div>","PeriodicalId":14061,"journal":{"name":"International Journal of Forecasting","volume":"40 3","pages":"Pages 1055-1068"},"PeriodicalIF":7.9,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135606286","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}