International Journal of Forecasting

1985 - 2025

Current editor(s): R. J. Hyndman

From Elsevier
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Volume 38, issue 4, 2022

Retail forecasting: Research and practice pp. 1283-1318

Robert Fildes, Shaohui Ma and Stephan Kolassa

Post-script—Retail forecasting: Research and practice pp. 1319-1324

Robert Fildes, Stephan Kolassa and Shaohui Ma

The M5 competition: Background, organization, and implementation pp. 1325-1336

Spyros Makridakis, Evangelos Spiliotis and Vassilios Assimakopoulos

Predicting/hypothesizing the findings of the M5 competition pp. 1337-1345

Spyros Makridakis, Evangelos Spiliotis and Vassilios Assimakopoulos

M5 accuracy competition: Results, findings, and conclusions pp. 1346-1364

Spyros Makridakis, Evangelos Spiliotis and Vassilios Assimakopoulos

The M5 uncertainty competition: Results, findings and conclusions pp. 1365-1385

Spyros Makridakis, Evangelos Spiliotis, Vassilios Assimakopoulos, Zhi Chen, Anil Gaba, Ilia Tsetlin and Robert L. Winkler

Simple averaging of direct and recursive forecasts via partial pooling using machine learning pp. 1386-1399

YeonJun In and Jae-Yoon Jung

A fast and scalable ensemble of global models with long memory and data partitioning for the M5 forecasting competition pp. 1400-1404

Kasun Bandara, Hansika Hewamalage, Rakshitha Godahewa and Puwasala Gamakumara

Hierarchical forecasting with a top-down alignment of independent-level forecasts pp. 1405-1414

Matthias Anderer and Feng Li

Robust recurrent network model for intermittent time-series forecasting pp. 1415-1425

Yunho Jeon and Sihyeon Seong

Forecasting with gradient boosted trees: augmentation, tuning, and cross-validation strategies pp. 1426-1433

A. David Lainder and Russell D. Wolfinger

GoodsForecast second-place solution in M5 Uncertainty track: Combining heterogeneous models for a quantile estimation task pp. 1434-1441

Nikolay Mamonov, Evgeny Golubyatnikov, Daniel Kanevskiy and Igor Gusakov

A solution for M5 Forecasting - Uncertainty: Hybrid gradient boosting and autoregressive recurrent neural network for quantile estimation pp. 1442-1447

Ernest Chiew and Shin Siang Choong

Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series pp. 1448-1459

Ioannis Nasios and Konstantinos Vogklis

A white-boxed ISSM approach to estimate uncertainty distributions of Walmart sales pp. 1460-1467

Rafael de Rezende, Katharina Egert, Ignacio Marin and Guilherme Thompson

Applicability of the M5 to Forecasting at Walmart pp. 1468-1472

Brian Seaman and John Bowman

Forecasting with trees pp. 1473-1481

Tim Januschowski, Yuyang Wang, Kari Torkkola, Timo Erkkilä, Hilaf Hasson and Jan Gasthaus

Transfer learning for hierarchical forecasting: Reducing computational efforts of M5 winning methods pp. 1482-1491

Arnoud P. Wellens, Maxi Udenio and Robert N. Boute

The performance of the global bottom-up approach in the M5 accuracy competition: A robustness check pp. 1492-1499

Shaohui Ma and Robert Fildes

Exploring the representativeness of the M5 competition data pp. 1500-1506

Evangelos Theodorou, Shengjie Wang, Yanfei Kang, Evangelos Spiliotis, Spyros Makridakis and Vassilios Assimakopoulos

Exploring the social influence of the Kaggle virtual community on the M5 competition pp. 1507-1518

Xixi Li, Yun Bai and Yanfei Kang

Fathoming empirical forecasting competitions’ winners pp. 1519-1525

Azzam Alroomi, Georgios Karamatzanis, Konstantinos Nikolopoulos, Anna Tilba and Shujun Xiao

The uncertainty track: Machine learning, statistical modeling, synthesis pp. 1526-1530

John Ord

Evaluating quantile forecasts in the M5 uncertainty competition pp. 1531-1545

Zhi Chen, Anil Gaba, Ilia Tsetlin and Robert L. Winkler

M5 competition uncertainty: Overdispersion, distributional forecasting, GAMLSS, and beyond pp. 1546-1554

Florian Ziel

Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities pp. 1555-1561

Casper Solheim Bojer

Commentary on the M5 forecasting competition pp. 1562-1568

Stephan Kolassa

Volume 38, issue 3, 2022

Forecasting: theory and practice pp. 705-871

Fotios Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos, Mohamed Zied Babai, Devon K. Barrow, Souhaib Ben Taieb, Christoph Bergmeir, Ricardo J. Bessa, Jakub Bijak, John E. Boylan, Jethro Browell, Claudio Carnevale, Jennifer Castle, Pasquale Cirillo, Michael Clements, Clara Cordeiro, Fernando Luiz Cyrino Oliveira, Shari De Baets, Alexander Dokumentov, Joanne Ellison, Piotr Fiszeder, Philip Hans Franses, David T. Frazier, Michael Gilliland, M. Sinan Gönül, Paul Goodwin, Luigi Grossi, Yael Grushka-Cockayne, Mariangela Guidolin, Massimo Guidolin, Ulrich Gunter, Xiaojia Guo, Renato Guseo, Nigel Harvey, David Hendry, Ross Hollyman, Tim Januschowski, Jooyoung Jeon, Victor Richmond R. Jose, Yanfei Kang, Anne B. Koehler, Stephan Kolassa, Nikolaos Kourentzes, Sonia Leva, Feng Li, Konstantia Litsiou, Spyros Makridakis, Gael M. Martin, Andrew Martinez, Sheik Meeran, Theodore Modis, Konstantinos Nikolopoulos, Dilek Önkal, Alessia Paccagnini, Anastasios Panagiotelis, Ioannis Panapakidis, Jose M. Pavía, Manuela Pedio, Diego J. Pedregal, Pierre Pinson, Patrícia Ramos, David E. Rapach, J Reade, Bahman Rostami-Tabar, Michał Rubaszek, Georgios Sermpinis, Han Lin Shang, Evangelos Spiliotis, Aris A. Syntetos, Priyanga Dilini Talagala, Thiyanga S. Talagala, Len Tashman, Dimitrios Thomakos, Thordis Thorarinsdottir, Ezio Todini, Juan Ramón Trapero Arenas, Xiaoqian Wang, Robert L. Winkler, Alisa Yusupova and Florian Ziel

In-sample tests of predictability are superior to pseudo-out-of-sample tests, even when data mining pp. 872-877

Ian Hunt

Forecasting cryptocurrency volatility pp. 878-894

Leopoldo Catania and Stefano Grassi

Forecasting football results and exploiting betting markets: The case of “both teams to score” pp. 895-909

Igor Barbosa da Costa, Leandro Balby Marinho and Carlos Eduardo Santos Pires

Cyberattack-resilient load forecasting with adaptive robust regression pp. 910-919

Jieying Jiao, Zefan Tang, Peng Zhang, Meng Yue and Jun Yan

FFORMPP: Feature-based forecast model performance prediction pp. 920-943

Thiyanga S. Talagala, Feng Li and Yanfei Kang

Forecasting European carbon returns using dimension reduction techniques: Commodity versus financial fundamentals pp. 944-969

Xueping Tan, Kavita Sirichand, Andrew Vivian and Xinyu Wang

A data-driven approach to forecasting ground-level ozone concentration pp. 970-987

Dario Marvin, Lorenzo Nespoli, Davide Strepparava and Vasco Medici

Context effects in inflation surveys: The influence of additional information and prior questions pp. 988-1004

Xiaoxiao Niu and Nigel Harvey

Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN pp. 1005-1024

Chuan Zhang, Yu-Xin Tian and Zhi-Ping Fan

Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces pp. 1025-1049

Han Lin Shang and Fearghal Kearney

Correction to: Optimal and robust combination of forecasts via constrained optimization and shrinkage pp. 1050-1050

Francesco Roccazzella, Paolo Gambetti and Frédéric Vrins

Forecasting corporate default risk in China pp. 1054-1070

Xuan Zhang, Yang Zhao and Xiao Yao

Spatial dependence in microfinance credit default pp. 1071-1085

Victor Medina-Olivares, Raffaella Calabrese, Yizhe Dong and Baofeng Shi

Mining semantic features in current reports for financial distress prediction: Empirical evidence from unlisted public firms in China pp. 1086-1099

Cuiqing Jiang, Ximei Lyu, Yufei Yuan, Zhao Wang and Yong Ding

The recurrence of financial distress: A survival analysis pp. 1100-1115

Fanyin Zhou, Lijun Fu, Zhiyong Li and Jiawei Xu

Sequential optimization three-way decision model with information gain for credit default risk evaluation pp. 1116-1128

Feng Shen, Xin Zhang, Run Wang, Dao Lan and Wei Zhou

A flexible framework for intervention analysis applied to credit-card usage during the coronavirus pandemic pp. 1129-1157

Anson Ho, Lealand Morin, Harry Paarsch and Kim Huynh

Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach pp. 1158-1172

Yue Sun, Nana Chai, Yizhe Dong and Baofeng Shi

Using scenarios to forecast outcomes of a refugee crisis pp. 1175-1184

Lars Wicke, Mandeep K. Dhami, Dilek Önkal and Ian K. Belton

Relative performance of judgmental methods for forecasting the success of megaprojects pp. 1185-1196

Konstantia Litsiou, Yiannis Polychronakis, Azhdar Karami and Konstantinos Nikolopoulos

Anticipating special events in Emergency Department forecasting pp. 1197-1213

Bahman Rostami-Tabar and Florian Ziel

A disaster response model driven by spatial–temporal forecasts pp. 1214-1220

Konstantinos Nikolopoulos, Fotios Petropoulos, Vasco Sanchez Rodrigues, Stephen Pettit and Anthony Beresford

Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction pp. 1221-1233

Johannes Bracher and Leonhard Held

Forecasting in humanitarian operations: Literature review and research needs pp. 1234-1244

Nezih Altay and Arunachalam Narayanan

Forecasting for social good pp. 1245-1257

Bahman Rostami-Tabar, Mohammad M. Ali, Tao Hong, Rob Hyndman, Michael D. Porter and Aris Syntetos

Machine learning algorithms for forecasting and backcasting blood demand data with missing values and outliers: A study of Tema General Hospital of Ghana pp. 1258-1277

Clement Twumasi and Juliet Twumasi

Volume 38, issue 2, 2022

Pandemics and forecasting: The way forward through the Taleb-Ioannidis debate pp. 410-412

Pierre Pinson and Spyros Makridakis

On single point forecasts for fat-tailed variables pp. 413-422

Nassim Nicholas Taleb, Yaneer Bar-Yam and Pasquale Cirillo

Forecasting for COVID-19 has failed pp. 423-438

John P.A. Ioannidis, Sally Cripps and Martin A. Tanner

COVID-19: Forecasting confirmed cases and deaths with a simple time series model pp. 439-452

Fotios Petropoulos, Spyros Makridakis and Neophytos Stylianou

Short-term forecasting of the coronavirus pandemic pp. 453-466

Jurgen Doornik, Jennifer Castle and David Hendry

Short-term Covid-19 forecast for latecomers pp. 467-488

Marcelo Medeiros, Alexandre Street, Davi Valladão, Gabriel Vasconcelos and Eduardo Zilberman

Comparing the accuracy of several network-based COVID-19 prediction algorithms pp. 489-504

Massimo A. Achterberg, Bastian Prasse, Long Ma, Stojan Trajanovski, Maksim Kitsak and Piet Van Mieghem

Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates pp. 505-520

Wen-Hao Chiang, Xueying Liu and George Mohler

Epidemiological versus meteorological forecasts: Best practice for linking models to policymaking pp. 521-526

Erin Coughlan de Perez, Elisabeth Stephens, Maarten van Aalst, Juan Bazo, Eleonore Fournier-Tombs, Sebastian Funk, Jeremy J. Hess, Nicola Ranger and Rachel Lowe

Guest editorial: Economic forecasting in times of COVID-19 pp. 527-528

Laurent Ferrara and Xuguang Simon Sheng

The impact of the COVID-19 pandemic on business expectations pp. 529-544

Brent Meyer, Brian Prescott and Xuguang Simon Sheng

Boosting tax revenues with mixed-frequency data in the aftermath of COVID-19: The case of New York pp. 545-566

Kajal Lahiri and Cheng Yang

Forecasting unemployment insurance claims in realtime with Google Trends pp. 567-581

Daniel Aaronson, Scott Brave, R. Andrew Butters, Michael Fogarty, Daniel W. Sacks and Boyoung Seo

High-frequency monitoring of growth at risk pp. 582-595

Laurent Ferrara, Matteo Mogliani and Jean-Guillaume Sahuc

Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis pp. 596-612

Claudia Foroni, Massimiliano Marcellino and Dalibor Stevanovic

Transparent modeling of influenza incidence: Big data or a single data point from psychological theory? pp. 613-619

Konstantinos V. Katsikopoulos, Özgür Şimşek, Marcus Buckmann and Gerd Gigerenzer

Nowcasting unemployment insurance claims in the time of COVID-19 pp. 635-647

William Larson and Tara Sinclair

Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest pp. 648-661

Jordan Bakerman, Karl Pazdernik, Gizem Korkmaz and Alyson G. Wilson

Regional heterogeneity and U.S. presidential elections: Real-time 2020 forecasts and evaluation pp. 662-687

Rashad Ahmed and Mohammad Pesaran

What do forecasting rationales reveal about thinking patterns of top geopolitical forecasters? pp. 688-704

Christopher W. Karvetski, Carolyn Meinel, Daniel T. Maxwell, Yunzi Lu, Barbara A. Mellers and Philip E. Tetlock

Volume 38, issue 1, 2022

Predicting monthly biofuel production using a hybrid ensemble forecasting methodology pp. 3-20

Lean Yu, Shaodong Liang, Rongda Chen and Kin Keung Lai

Artificial bee colony-based combination approach to forecasting agricultural commodity prices pp. 21-34

Jue Wang, Zhen Wang, Xiang Li and Hao Zhou

A novel text-based framework for forecasting agricultural futures using massive online news headlines pp. 35-50

Jianping Li, Guowen Li, Mingxi Liu, Xiaoqian Zhu and Lu Wei

Forecasting realized volatility of agricultural commodity futures with infinite Hidden Markov HAR models pp. 51-73

Jiawen Luo, Tony Klein, Qiang Ji and Chenghan Hou

Forecasting realized volatility of agricultural commodities pp. 74-96

Stavros Degiannakis, George Filis, Tony Klein and Thomas Walther

Optimal and robust combination of forecasts via constrained optimization and shrinkage pp. 97-116

Francesco Roccazzella, Paolo Gambetti and Frédéric Vrins

Forecasting in GARCH models with polynomially modified innovations pp. 117-141

Gianmarco Vacca, Maria Zoia and Luca Bagnato

Forecast combination for VARs in large N and T panels pp. 142-164

Ryan Greenaway-McGrevy

The kernel trick for nonlinear factor modeling pp. 165-177

Varlam Kutateladze

Enhancing capacity planning through forecasting: An integrated tool for maintenance of complex product systems pp. 178-192

Duarte Dinis, Ana Barbosa-Póvoa and Ângelo Palos Teixeira

Combining forecasts for universally optimal performance pp. 193-208

Wei Qian, Craig A. Rolling, Gang Cheng and Yuhong Yang

Classification-based model selection in retail demand forecasting pp. 209-223

Matthias Ulrich, Hermann Jahnke, Roland Langrock, Robert Pesch and Robin Senge

Nonparametric expected shortfall forecasting incorporating weighted quantiles pp. 224-239

Giuseppe Storti and Chao Wang

Deep learning for modeling the collection rate for third-party buyers pp. 240-252

Abdolreza Nazemi, Hani Rezazadeh, Frank J. Fabozzi and Markus Höchstötter

Reducing revisions in hedonic house price indices by the use of nowcasts pp. 253-266

Doron Sayag, Dano Ben-hur and Danny Pfeffermann

Comparing probabilistic forecasts of the daily minimum and maximum temperature pp. 267-281

Xiaochun Meng and James W. Taylor

Informational efficiency and behaviour within in-play prediction markets pp. 282-299

Giovanni Angelini, Luca De Angelis and Carl Singleton

Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model pp. 300-320

Mario Arrieta-Prieto and Kristen R. Schell

A Bayesian approach for predicting food and beverage sales in staff canteens and restaurants pp. 321-338

Konstantin Posch, Christian Truden, Philipp Hungerländer and Jürgen Pilz

Online hierarchical forecasting for power consumption data pp. 339-351

Margaux Brégère and Malo Huard

Random coefficient state-space model: Estimation and performance in M3–M4 competitions pp. 352-366

Giacomo Sbrana and Andrea Silvestrini

Crude oil price forecasting incorporating news text pp. 367-383

Yun Bai, Xixi Li, Hao Yu and Suling Jia

Optimal probabilistic forecasts: When do they work? pp. 384-406

Gael M. Martin, Rubén Loaiza-Maya, Worapree Maneesoonthorn, David T. Frazier and Andrés Ramírez-Hassan