International Journal of Forecasting

1985 - 2025

Current editor(s): R. J. Hyndman

From Elsevier
Bibliographic data for series maintained by Catherine Liu ().

Access Statistics for this journal.
Is something missing from the series or not right? See the RePEc data check for the archive and series.

Volume 36, issue 4, 2020

Probabilistic forecasting in day-ahead electricity markets: Simulating peak and off-peak prices pp. 1193-1210

Peru Muniain and Florian Ziel

Forecasting using heterogeneous panels with cross-sectional dependence pp. 1211-1227

Oguzhan Akgun, Alain Pirotte and Giovanni Urga

On the statistical differences between binary forecasts and real-world payoffs pp. 1228-1240

Nassim Nicholas Taleb

Agustín Maravall: An interview with the International Journal of Forecasting pp. 1241-1251

Daniel Peña

Data revisions to German national accounts: Are initial releases good nowcasts? pp. 1252-1259

Till Strohsal and Elias Wolf

Statistical learning and exchange rate forecasting pp. 1260-1289

Emilio Colombo and Matteo Pelagatti

Investigating the inefficiency of the CBO’s budgetary projections pp. 1290-1300

Natsuki Arai

Forecasting volatility with time-varying leverage and volatility of volatility effects pp. 1301-1317

Leopoldo Catania and Tommaso Proietti

Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts pp. 1318-1328

Bo Zhang, Joshua Chan and Jamie Cross

Extension of the Elo rating system to margin of victory pp. 1329-1341

Stephanie Kovalchik

Demand forecasting under fill rate constraints—The case of re-order points pp. 1342-1361

Joanna Bruzda

Forecasting value at risk and expected shortfall with mixed data sampling pp. 1362-1379

Trung H. Le

A strategic predictive distribution for tests of probabilistic calibration pp. 1380-1388

James W. Taylor

Automatic Interpretable Retail forecasting with promotional scenarios pp. 1389-1406

Özden Gür Ali and Ragıp Gürlek

A two-stage model to forecast elections in new democracies pp. 1407-1419

Kenneth Bunker

Daily retail demand forecasting using machine learning with emphasis on calendric special days pp. 1420-1438

Jakob Huber and Heiner Stuckenschmidt

Do macroeconomic forecasters use macroeconomics to forecast? pp. 1439-1453

Eddie Casey

Forecasting global equity market volatilities pp. 1454-1475

Yaojie Zhang, Feng Ma and Yin Liao

A textual analysis of Bank of England growth forecasts pp. 1478-1487

Jacob T. Jones, Tara Sinclair and Herman Stekler

Forecasting and forecast narratives: The Bank of England Inflation Reports pp. 1488-1500

Michael Clements and J Reade

Forecasting with news sentiment: Evidence with UK newspapers pp. 1501-1516

Dooruj Rambaccussing and Andrzej Kwiatkowski

Linking words in economic discourse: Implications for macroeconomic forecasts pp. 1517-1530

J. Daniel Aromi

GDP forecasts: Informational asymmetry of the SPF and FOMC minutes pp. 1531-1540

Olga Bespalova

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning pp. 1541-1562

Yelin Li, Hui Bu, Jiahong Li and JunJie Wu

Incorporating textual information in customer churn prediction models based on a convolutional neural network pp. 1563-1578

Arno De Caigny, Kristof Coussement, Koen W. De Bock and Stefan Lessmann

Volume 36, issue 3, 2020

Forecasting third-party mobile payments with implications for customer flow prediction pp. 739-760

Shaohui Ma and Robert Fildes

Bias corrections for exponentially transformed forecasts: Are they worth the effort? pp. 761-780

Matei Demetrescu, Vasyl Golosnoy and Anna Titova

Realized volatility forecast with the Bayesian random compressed multivariate HAR model pp. 781-799

Jiawen Luo and Langnan Chen

An information-theoretic approach for forecasting interval-valued SP500 daily returns pp. 800-813

T.S. Tuang Buansing, Amos Golan and Aman Ullah

Forecasting the urban skyline with extreme value theory pp. 814-828

Jonathan Auerbach and Phyllis Wan

Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model pp. 829-850

Kai Carstensen, Markus Heinrich, Magnus Reif and Maik Wolters

A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth pp. 851-872

Tony Chernis, Calista Cheung and Gabriella Velasco

A Model Confidence Set approach to the combination of multivariate volatility forecasts pp. 873-891

Alessandra Amendola, Manuela Braione, Vincenzo Candila and Giuseppe Storti

Election forecasts: Cracking the Danish case pp. 892-898

Richard Nadeau and Michael S. Lewis-Beck

Macroeconomic forecasting with large Bayesian VARs: Global-local priors and the illusion of sparsity pp. 899-915

Jamie Cross, Chenghan Hou and Aubrey Poon

A profitable model for predicting the over/under market in football pp. 916-932

Edward Wheatcroft

Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks pp. 933-948

Manabu Asai, Rangan Gupta and Michael McAleer

Election forecasting: Too far out? pp. 949-962

Will Jennings, Michael Lewis-Beck and Christopher Wlezien

Are GDP forecasts optimal? Evidence on European countries pp. 963-973

Alessandro Giovannelli and Filippo Maria Pericoli

Comparing the forecasting performances of linear models for electricity prices with high RES penetration pp. 974-986

Angelica Gianfreda, Francesco Ravazzolo and Luca Rossini

Measuring public opinion via digital footprints pp. 987-1002

Roberto Cerina and Raymond Duch

Predicting LGD distributions with mixed continuous and discrete ordinal outcomes pp. 1003-1022

Ruey-Ching Hwang, Chih-Kang Chu and Kaizhi Yu

Forecasting value at risk with intra-day return curves pp. 1023-1038

Gregory Rice, Tony Wirjanto and Yuqian Zhao

Predicting default risk under asymmetric binary link functions pp. 1039-1056

Yiannis Dendramis, Elias Tzavalis, Petros Varthalitis and E. Athanasiou

Forecasting risk measures using intraday data in a generalized autoregressive score framework pp. 1057-1072

Emese Lazar and Xiaohan Xue

Bayesian loss given default estimation for European sovereign bonds pp. 1073-1091

Rainer Jobst, Ralf Kellner and Daniel Rösch

Predicting bank insolvencies using machine learning techniques pp. 1092-1113

Anastasios Petropoulos, Vasilis Siakoulis, Evangelos Stavroulakis and Nikolaos E. Vlachogiannakis

Efficient big data model selection with applications to fraud detection pp. 1116-1127

Gregory Vaughan

Brexit: Tracking and disentangling the sentiment towards leaving the EU pp. 1128-1137

Miguel de Carvalho and Gabriel Martos

Spatio-temporal modeling of yellow taxi demands in New York City using generalized STAR models pp. 1138-1148

Abolfazl Safikhani, Camille Kamga, Sandeep Mudigonda, Sabiheh Sadat Faghih and Bahman Moghimi

Quantile forecasting with mixed-frequency data pp. 1149-1162

Luiz Lima, Fanning Meng and Lucas Godeiro

Can Google search data help predict macroeconomic series? pp. 1163-1172

Robin F. Niesert, Jochem A. Oorschot, Christian P. Veldhuisen, Kester Brons and Rutger-Jan Lange

Nowcasting in real time using popularity priors pp. 1173-1180

George Monokroussos and Yongchen Zhao

DeepAR: Probabilistic forecasting with autoregressive recurrent networks pp. 1181-1191

David Salinas, Valentin Flunkert, Jan Gasthaus and Tim Januschowski

Volume 36, issue 2, 2020

Forecasting inflation with online prices pp. 232-247

Diego Aparicio and Manuel I. Bertolotto

Predicting loss given default in leasing: A closer look at models and variable selection pp. 248-266

Florian Kaposty, Johannes Kriebel and Matthias Löderbusch

Macroeconomic forecasting using approximate factor models with outliers pp. 267-291

Ray Chou, Tso-Jung Yen and Yu-Min Yen

Forecasting bulk prices of Bordeaux wines using leading indicators pp. 292-309

Emmanuel Paroissien

Probabilistic energy forecasting using the nearest neighbors quantile filter and quantile regression pp. 310-323

Jorge Ángel González Ordiano, Lutz Gröll, Ralf Mikut and Veit Hagenmeyer

Temperature anomaly detection for electric load forecasting pp. 324-333

Masoud Sobhani, Tao Hong and Claude Martin

The impact of sentiment and attention measures on stock market volatility pp. 334-357

Francesco Audrino, Fabio Sigrist and Daniele Ballinari

High-frequency credit spread information and macroeconomic forecast revision pp. 358-372

Bruno Deschamps, Christos Ioannidis and Kook Ka

Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy pp. 373-398

Ellis Tallman and Saeed Zaman

Model-based pre-election polling for national and sub-national outcomes in the US and UK pp. 399-413

Benjamin E. Lauderdale, Delia Bailey, Jack Blumenau and Douglas Rivers

Forecasting election results by studying brand importance in online news pp. 414-427

Andrea Fronzetti Colladon

Forecast combinations for value at risk and expected shortfall pp. 428-441

James W. Taylor

Forecasting from others’ experience: Bayesian estimation of the generalized Bass model pp. 442-465

Andrés Ramírez-Hassan and Santiago Montoya-Blandón

Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts? pp. 466-479

Grzegorz Marcjasz, Bartosz Uniejewski and Rafał Weron

Crime prediction by data-driven Green’s function method pp. 480-488

Mami Kajita and Seiji Kajita

Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures pp. 489-506

Richard Gerlach and Chao Wang

Improved recession dating using stock market volatility pp. 507-514

Yu-Fan Huang and Richard Startz

Probabilistic wind forecasting up to three months ahead using ensemble predictions for geopotential height pp. 515-530

Bastien Alonzo, Peter Tankov, Philippe Drobinski and Riwal Plougonven

Comparing density forecasts in a risk management context pp. 531-551

Cees Diks and Hao Fang

Probabilistic forecasting of heterogeneous consumer transaction–sales time series pp. 552-569

Lindsay R. Berry, Paul Helman and Mike West

Oil price shocks and economic growth: The volatility link pp. 570-587

John Maheu, Yong Song and Qiao Yang

An empirical investigation of water consumption forecasting methods pp. 588-606

Panagiotis I. Karamaziotis, Achilleas Raptis, Konstantinos Nikolopoulos, Konstantia Litsiou and Vassilis Assimakopoulos

Five dimensions of the uncertainty–disagreement linkage pp. 607-627

Alexander Glas

Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks? pp. 628-645

Štefan Lyócsa and Neda Todorova

A functional time series analysis of forward curves derived from commodity futures pp. 646-665

Lajos Horvath, Zhenya Liu, Gregory Rice and Shixuan Wang

Forecasting commodity prices out-of-sample: Can technical indicators help? pp. 666-683

Yudong Wang, Li Liu and Chongfeng Wu

Forecasting stock price volatility: New evidence from the GARCH-MIDAS model pp. 684-694

Lu Wang, Feng Ma, Jing Liu and Lin Yang

Rethinking weather station selection for electric load forecasting using genetic algorithms pp. 695-712

Santiago Moreno-Carbonell, Eugenio F. Sánchez-Úbeda and Antonio Muñoz

Are betting returns a useful measure of accuracy in (sports) forecasting? pp. 713-722

Fabian Wunderlich and Daniel Memmert

The term structure of volatility predictability pp. 723-737

Xingyi Li and Valeriy Zakamulin

Volume 36, issue 1, 2020

A brief history of forecasting competitions pp. 7-14

Rob Hyndman

Forecasting in social settings: The state of the art pp. 15-28

Spyros Makridakis, Rob Hyndman and Fotios Petropoulos

Are forecasting competitions data representative of the reality? pp. 37-53

Evangelos Spiliotis, Andreas Kouloumos, Vassilios Assimakopoulos and Spyros Makridakis

The M4 Competition: 100,000 time series and 61 forecasting methods pp. 54-74

Spyros Makridakis, Evangelos Spiliotis and Vassilios Assimakopoulos

A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting pp. 75-85

Slawek Smyl

FFORMA: Feature-based forecast model averaging pp. 86-92

Pablo Montero-Manso, George Athanasopoulos, Rob Hyndman and Thiyanga S. Talagala

Weighted ensemble of statistical models pp. 93-97

Maciej Pawlikowski and Agata Chorowska

A combination-based forecasting method for the M4-competition pp. 98-104

Srihari Jaganathan and P.K.S. Prakash

GROEC: Combination method via Generalized Rolling Origin Evaluation pp. 105-109

Jose Augusto Fiorucci and Francisco Louzada

A simple combination of univariate models pp. 110-115

Fotios Petropoulos and Ivan Svetunkov

Fast and accurate yearly time series forecasting with forecast combinations pp. 116-120

David Shaub

Correlated daily time series and forecasting in the M4 competition pp. 121-128

Anti Ingel, Novin Shahroudi, Markus Kängsepp, Andre Tättar, Viacheslav Komisarenko and Meelis Kull

Card forecasts for M4 pp. 129-134

Jurgen Doornik, Jennifer Castle and David Hendry

Forecasting the M4 competition weekly data: Forecast Pro’s winning approach pp. 135-141

Sarah Goodrich Darin and Eric Stellwagen