Contact information of Elsevier
Serial Information
Download restrictions:
Full text for ScienceDirect subscribers only
Editor:R. J. Hyndman
Series handle:RePEc:eee:intfor
ISSN:0169-2070
Citations RSS feed: at CitEcImpact factors
Access and download statisticsTop item:Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intfor. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .
Content
2026, Volume 42, Issue 2
- 297-314 Can we protect time series data while maintaining accurate forecasts?
by Bale, Cameron D. & Schneider, Matthew J. & Lee, Jinwook - 315-329 Practice makes perfect: Learning effects with household point and density forecasts of inflation
by Mitchell, James & Shiroff, Taylor & Braitsch, Hana - 330-343 HARd to beat: The overlooked impact of rolling windows in the era of machine learning
by Chassot, Jonathan & Audrino, Francesco - 344-358 Using dynamic loss weighting to boost improvements in forecast stability
by Caljon, Daan & Vercauteren, Jeff & De Vos, Simon & Verbeke, Wouter & Van Belle, Jente - 359-413 Impact of climate change on mortality: An extrapolation of temperature effects based on time series data in France
by Guibert, Quentin & Pincemin, Gaëlle & Planchet, Frédéric - 414-433 Forecasting economic time series in the presence of weak factors: Multiple supervised learning-based approach
by Hounyo, Ulrich & Li, Zhendong - 434-456 Portfolio return prediction and risk price heterogeneity
by Taylor, Nick - 457-473 Ups and (draw) downs
by Proietti, Tommaso - 474-491 The Bayesian context trees state space model for time series modelling and forecasting
by Papageorgiou, Ioannis & Kontoyiannis, Ioannis - 492-511 Bayesian estimation of a multivariate TAR model when the noise process distribution belongs to the class of Gaussian variance mixtures
by Vanegas, L.H. & Calderón V, S.A. & Rondón, L.M. - 512-526 Overreaction through anchoring
by Bürgi, Constantin & Ortiz, Julio L. - 527-547 Macroeconomic forecasting using factor models with martingale difference errors
by Rolla, L.M. & Giovannelli, A. - 548-569 Getting back on track: Forecasting after extreme observations
by Boug, Pål & Hungnes, Håvard & Kurita, Takamitsu - 570-586 Volatility forecasting for low-volatility investing
by Conrad, Christian & Kleen, Onno & Lönn, Rasmus - 587-601 Whispers in the oil market: Exploring sentiment and uncertainty insights
by Gifuni, Luigi - 602-615 Forecasting electoral violence
by Randahl, David & Leis, Maxine & Gåsste, Tim & Fjelde, Hanne & Hegre, Håvard & Lindberg, Staffan I. & Wilson, Steven - 616-639 Leveraging image-based generative adversarial networks for time series generation
by Hellermann, Justin & Lessmann, Stefan - 640-656 Asymmetric models for realized covariances
by Bauwens, Luc & Dzuverovic, Emilija & Hafner, Christian - 657-672 Nowcasting economic activity in European regions using a mixed-frequency dynamic factor model
by Barbaglia, Luca & Frattarolo, Lorenzo & Hauzenberger, Niko & Hirschbühl, Dominik & Huber, Florian & Onorante, Luca & Pfarrhofer, Michael & Pezzoli, Luca Tiozzo - 673-690 Combining predictive distributions for time-to-event outcomes in meteorology
by Cunen, Céline & Roksvåg, Thea & Heinrich-Mertsching, Claudio & Lenkoski, Alex - 691-707 Stochastic modelling of football matches using dynamic regressors
by Maia, Luiz Fernando G.N. & Pennanen, Teemu & da Silva, Moacyr A.H.B. & Targino, Rodrigo S.
2026, Volume 42, Issue 1
- 1-2 Editorial and introduction to the special section on the Bernanke’s review of the Bank of England’s forecasting activities
by Pinson, Pierre - 3-12 Reactions to the Bernanke Review from Bank of England watchers
by Aikman, David & Barwell, Richard - 13-21 Could the Bank of England have avoided mis-forecasting UK inflation during 2021–24?
by Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F. - 22-33 Forecasting for monetary policy
by Coroneo, Laura - 34-39 Forecasting and policy when “we simply do not know”
by Kirman, Alan & Armstrong, Angus & Hynes, William - 40-43 Beyond the numbers: The role of people and processes in central bank forecasting
by Kourentzes, Nikolaos & Fildes, Robert - 44-60 Optimal text-based time-series indices
by Ardia, David & Bluteau, Keven - 61-84 When to be discrete: The importance of time formulation in the modeling of extreme events in finance
by Bień-Barkowska, Katarzyna & Herrera, Rodrigo - 85-98 Deep switching state space model for nonlinear time series forecasting with regime switching
by Xu, Xiuqin & Peng, Hanqiu & Chen, Ying - 99-111 Is it possible to predict electoral abstention on the individual level? A preregistered test on forecasting the effects of abolishing compulsory voting in Belgium
by Stiers, Dieter & Hooghe, Marc - 112-125 Kairosis: A method for dynamical probability forecast aggregation informed by Bayesian change-point detection
by Hassoun, Zane & MacKay, Niall & Powell, Ben - 126-137 Combining forecasts under structural breaks using Graphical LASSO
by Lee, Tae-Hwy & Seregina, Ekaterina - 138-157 Anticipating humanitarian emergencies with a high risk of conflict-induced displacement
by Rost, Nicolas & Ronco, Michele - 158-180 A functional mixture prediction model for dynamically forecasting cumulative intraday returns of crude oil futures
by Wang, Deqing & Lu, Zhihao & Liu, Zhenhua & Xue, Shoucong & Guo, Mengxia & Hou, Yiwen - 181-202 Forecasting UK consumer price inflation with RaGNAR: Random generalised network autoregressive processes
by Nason, Guy P. & Palasciano, Henry Antonio - 203-215 Citizen forecasting in a mixed electoral system
by Leininger, Arndt & Murr, Andreas E. & Stötzer, Lukas & Kayser, Mark A. - 216-234 Hierarchical neural additive models for interpretable demand forecasts
by Feddersen, Leif & Cleophas, Catherine - 235-258 All forecasters are not the same: Systematic patterns in predictive performance
by Rich, Robert W. & Tracy, Joseph - 259-280 VAR Model with Sparse Group LASSO for Multi-population Mortality Forecasting
by Boonen, Tim J. & Chen, Yuhuai - 281-295 Carpe diem: Can daily oil prices improve model-based forecasts of the real price of crude oil?
by Benmoussa, Amor Aniss & Ellwanger, Reinhard & Snudden, Stephen
2025, Volume 41, Issue 4
- 1311-1314 Introduction to the M6 forecasting competition Special Issue
by Makridakis, Spyros & Petropoulos, Fotios & Spiliotis, Evangelos & Swanson, Norman R. - 1315-1354 The M6 forecasting competition: Bridging the gap between forecasting and investment decisions
by Makridakis, Spyros & Spiliotis, Evangelos & Hollyman, Ross & Petropoulos, Fotios & Swanson, Norman & Gaba, Anil - 1355-1382 A survey of models and methods used for forecasting when investing in financial markets
by Maung, Kenwin & Swanson, Norman R. - 1383-1394 Benchmarking M6 competitors: An analysis of financial metrics and discussion of incentives
by Schneider, Matthew J. & Rankin, Rufus & Burman, Prabir & Aue, Alexander - 1395-1403 Avoiding overconfidence: Evidence from the M6 financial competition
by Makridakis, Spyros & Spiliotis, Evangelos & Michailidis, Maria - 1404-1412 Unraveling the effect of engagement and consistency in the results of the M6 forecasting competition
by Kaltsounis, Anastasios & Theodorou, Evangelos & Spiliotis, Evangelos & Assimakopoulos, Vassilios - 1413-1427 M6 investment challenge: The role of luck and strategic considerations
by Staněk, Filip - 1428-1449 Predicting the relative performance among financial assets: A comparative analysis of different approaches
by Samartzis, Panagiotis - 1450-1460 Single-stage portfolio optimization with automated machine learning for M6
by Huang, Xinyu & Newton, David P. & Platanakis, Emmanouil & Sutcliffe, Charles - 1461-1476 Designing time-series models with hypernetworks and adversarial portfolios
by Staněk, Filip - 1477-1484 Probabilistic forecasting of cross-sectional returns: A Bayesian dynamic factor model with heteroskedasticity
by Weitzenfeld, Dan - 1485-1493 Adaptive forecasting in dynamic markets: An evaluation of AutoTS within the M6 competition
by Catlin, Colin - 1494-1504 Robust returns ranking prediction and portfolio optimization for M6
by Ai, Hongfeng & Liu, Chenning & Lin, Peng - 1505-1513 Quasi-average predictions and regression to the trend: An application to the M6 financial forecasting competition
by Vilar, Jose M.G. - 1514-1520 An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition
by de Vilmarest, Joseph & Werge, Nicklas - 1521-1538 Individual foresight: Concept, operationalization, and correlates
by Schuler, Benedikt Alexander & Murmann, Johann Peter & Beisemann, Marie & Satopää, Ville - 1539-1558 On forecast stability
by Godahewa, Rakshitha & Bergmeir, Christoph & Erkin Baz, Zeynep & Zhu, Chengjun & Song, Zhangdi & García, Salvador & Benavides, Dario - 1559-1588 Disaggregating VIX
by Degiannakis, Stavros & Kafousaki, Eleftheria - 1589-1619 Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends!
by Gruber, Luis & Kastner, Gregor - 1620-1635 Predicting Australian federal electoral seats with machine learning
by Collins, John ‘Jack’ - 1636-1652 SCORE: A convolutional approach for football event forecasting
by Alves, Rodrigo - 1655-1665 Election forecasting: Political economy models
by Lewis-Beck, Michael S. & Kenny, John & Leiter, Debra & Murr, Andreas Erwin & Ogili, Onyinye B. & Stegmaier, Mary & Tien, Charles - 1666-1695 Deep learning and NLP in cryptocurrency forecasting: Integrating financial, blockchain, and social media data
by Gurgul, Vincent & Lessmann, Stefan & Härdle, Wolfgang Karl
2025, Volume 41, Issue 3
- 863-876 Trust the experts? The performance of inflation expectations, 1960–2023
by Goodspeed, Tyler - 877-893 The decrease in confidence with forecast extremity
by Sonsino, Doron & Roth, Yefim - 894-919 Credit scoring model for fintech lending: An integration of large language models and FocalPoly loss
by Xia, Yufei & Han, Zhiyin & Li, Yawen & He, Lingyun - 920-939 A semi-supervised reject inference framework with hierarchical heterogeneous networks for credit scoring
by Chen, Liao & Jia, Ning & Jiao, Zhixian & Zhao, Hongke & Cui, Runbang & Wang, Huimin - 940-952 The structural Theta method and its predictive performance in the M4-Competition
by Sbrana, Giacomo & Silvestrini, Andrea - 953-981 Forecasting CPI inflation under economic policy and geopolitical uncertainties
by Sengupta, Shovon & Chakraborty, Tanujit & Singh, Sunny Kumar - 982-1002 Time-varying parameters as ridge regressions
by Goulet Coulombe, Philippe - 1003-1021 Fundamental determinants of exchange rate expectations
by Beckmann, Joscha & Czudaj, Robert L. - 1022-1036 Constructing hierarchical time series through clustering: Is there an optimal way for forecasting?
by Zhang, Bohan & Panagiotelis, Anastasios & Li, Han - 1037-1054 Interpretable water level forecaster with spatiotemporal causal attention mechanisms
by Hong, Sungchul & Choi, Yunjin & Jeon, Jong-June - 1055-1072 Econometric forecasting using ubiquitous news text: Text-enhanced factor model
by Seo, Beomseok - 1073-1092 Testing for equal predictive accuracy with strong dependence
by Coroneo, Laura & Iacone, Fabrizio - 1093-1111 SpotV2Net: Multivariate intraday spot volatility forecasting via vol-of-vol-informed graph attention networks
by Brini, Alessio & Toscano, Giacomo - 1112-1125 Decision-focused linear pooling for probabilistic forecast combination
by Stratigakos, Akylas & Pineda, Salvador & Morales, Juan Miguel - 1126-1147 Stock return predictability in the frequency domain
by Dai, Zhifeng & Jiang, Fuwei & Kang, Jie & Xue, Bowen - 1148-1164 Fan charts 2.0: Flexible forecast distributions with expert judgement
by Sokol, Andrej - 1165-1183 The contribution of realized variance–covariance models to the economic value of volatility timing
by Bauwens, Luc & Xu, Yongdeng - 1184-1198 Multivariate dynamic mixed-frequency density pooling for financial forecasting
by Virbickaitė, Audronė & Lopes, Hedibert F. & Zaharieva, Martina Danielova - 1199-1222 Predicting value at risk for cryptocurrencies with generalized random forests
by Buse, Rebekka & Görgen, Konstantin & Schienle, Melanie - 1223-1236 SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe
by Depoortere, Joris & Driesen, Johan & Suykens, Johan & Kazmi, Hussain Syed - 1237-1259 Modeling and predicting failure in US credit unions
by Peng, Qiao & McKillop, Donal & Quinn, Barry & Liu, Kailong - 1260-1277 Real-time monitoring procedures for early detection of bubbles
by Whitehouse, E.J. & Harvey, D.I. & Leybourne, S.J. - 1278-1295 Forecasting stock market return with anomalies: Evidence from China
by Wang, Jianqiu & Wang, Zhuo & Wu, Ke - 1296-1309 A projected nonlinear state-space model for forecasting time series signals
by Donner, Christian & Mishra, Anuj & Shimazaki, Hideaki
2025, Volume 41, Issue 2
- 419-423 Introduction to the Special Issue on Judgment in Forecasting
by Fildes, Robert & Bolger, Fergus & Goodwin, Paul & Harvey, Nigel & Seifert, Matthias - 424-439 An overview of the effects of algorithm use on judgmental biases affecting forecasting
by Chacon, Alvaro & Kaufmann, Esther - 440-451 Light-touch forecasting: A novel method to combine human judgment with statistical algorithms
by van der Staak, B.B.J.P.J. & Basten, R.J.I. & van de Calseyde, P.P.F.M. & Demerouti, E. & de Kok, A.G. - 452-465 Investigating laypeople’s short- and long-term forecasts of COVID-19 infection cycles
by Koo, Moon Su & Lee, Yun Shin & Seifert, Matthias - 466-474 An extended logarithmic visualization improves forecasting accuracy for exponentially growing numbers, but residual difficulties remain
by Engler, Ben H. & Hutzler, Florian & Hawelka, Stefan - 475-486 Judgment in macroeconomic output growth predictions: Efficiency, accuracy and persistence
by Pedersen, Michael - 487-498 How does training improve individual forecasts? Modeling differences in compensatory and non-compensatory biases in geopolitical forecasts
by Karimi Motahhar, Vahid & Gruca, Thomas S. - 499-516 Subjective-probability forecasts of existential risk: Initial results from a hybrid persuasion-forecasting tournament
by Karger, Ezra & Rosenberg, Josh & Jacobs, Zachary & Hickman, Molly & Tetlock, Phillip E. - 517-531 Cognitive reflection, arithmetic ability and financial literacy independently predict both inflation expectations and forecast accuracy
by Comerford, David A. - 532-553 Factors affecting preferences between judgmental and algorithmic forecasts: Feedback, guidance and labeling effects
by Harvey, Nigel & De Baets, Shari - 554-570 Service-level anchoring in demand forecasting: The moderating impact of retail promotions and product perishability
by Fahimnia, Ben & Tan, Tarkan & Tahirov, Nail - 571-579 Emotions and the status quo: The anti-incumbency bias in political prediction markets
by Karimi Motahhar, Vahid & Gruca, Thomas S. & Tavakoli, Mohammad Hosein - 580-595 Crowd prediction systems: Markets, polls, and elite forecasters
by Atanasov, Pavel & Witkowski, Jens & Mellers, Barbara & Tetlock, Philip - 596-612 Measuring probabilistic coherence to identify superior forecasters
by Ho, Emily H. & Budescu, David V. & Himmelstein, Mark - 613-630 Robust recalibration of aggregate probability forecasts using meta-beliefs
by Peker, Cem & Wilkening, Tom - 631-648 Humans vs. large language models: Judgmental forecasting in an era of advanced AI
by Abolghasemi, Mahdi & Ganbold, Odkhishig & Rotaru, Kristian - 649-669 Forecast value added in demand planning
by Fildes, Robert & Goodwin, Paul & De Baets, Shari - 670-688 Efficiency of poll-based multi-period forecasting systems for German state elections
by Fritsch, Markus & Haupt, Harry & Schnurbus, Joachim - 689-701 Improving out-of-population prediction: The complementary effects of model assistance and judgmental bootstrapping
by Hardy, Mathew D. & Zhang, Sam & Hullman, Jessica & Hofman, Jake M. & Goldstein, Daniel G. - 702-715 Partisan bias, attribute substitution, and the benefits of an indirect format for eliciting forecasts and judgments of trend
by Comerford, David A. & Soll, Jack B. - 716-732 Guiding supervisors in artificial intelligence-enabled forecasting: Understanding the impacts of salience and detail on decision-making
by Khosrowabadi, Naghmeh & Hoberg, Kai & Lee, Yun Shin - 733-747 Adaptively aggregated forecast for exponential family panel model
by Yu, Dalei & Tang, Nian-Sheng & Shi, Yang - 748-762 Does economic uncertainty predict real activity in real time?
by Keijsers, Bart & van Dijk, Dick - 763-780 Skew–Brownian processes for estimating the volatility of crude oil Brent
by Bufalo, Michele & Liseo, Brunero & Orlando, Giuseppe - 781-797 Sensitivity and uncertainty in the Lee–Carter mortality model
by Zuo, Wenyun & Damle, Anil & Tuljapurkar, Shripad - 798-802 Not feeling the buzz: Correction study of mispricing and inefficiency in online sportsbooks
by Clegg, Lawrence & Cartlidge, John - 803-820 Forecasting soccer matches with betting odds: A tale of two markets
by Hegarty, Tadgh & Whelan, Karl - 821-843 A framework for timely and accessible long-term forecasting of shale gas production based on time series pattern matching
by Dong, Yilun & Hao, Youzhi & Lu, Detang - 844-858 On memory-augmented gated recurrent unit network
by Yang, Maolin & Li, Muyi & Li, Guodong
2025, Volume 41, Issue 1
- 1-2 Guest editorial: Forecasting for social good
by Rostami-Tabar, Bahman & Pinson, Pierre & Porter, Michael D. - 3-19 Machine learning for satisficing operational decision making: A case study in blood supply chain
by Abolghasemi, Mahdi & Abbasi, Babak & HosseiniFard, Zahra - 20-30 Nowcasting U.S. state-level CO2 emissions and energy consumption
by Fosten, Jack & Nandi, Shaoni - 31-50 Predicting and optimizing the fair allocation of donations in hunger relief supply chains
by Sharmile, Nowshin & Nuamah, Isaac A. & Davis, Lauren & Samanlioglu, Funda & Jiang, Steven & Crain, Carter - 51-65 Forecasting mail flow: A hierarchical approach for enhanced societal wellbeing
by Kafa, Nadine & Babai, M. Zied & Klibi, Walid - 66-75 Forecasting presidential elections: Accuracy of ANES voter intentions
by Ko, Hyein & Jackson, Natalie & Osborn, Tracy & Lewis-Beck, Michael S. - 76-91 Forecasting adversarial actions using judgment decomposition-recomposition
by Gomez, Yolanda & Rios, Jesus & Rios Insua, David & Vila, Jose - 92-110 Return predictability, dividend growth, and the persistence of the price–dividend ratio
by Goliński, Adam & Madeira, João & Rambaccussing, Dooruj - 111-127 Local and global trend Bayesian exponential smoothing models
by Smyl, Slawek & Bergmeir, Christoph & Dokumentov, Alexander & Long, Xueying & Wibowo, Erwin & Schmidt, Daniel - 128-140 Coupling LSTM neural networks and state-space models through analytically tractable inference
by Vuong, Van-Dai & Nguyen, Luong-Ha & Goulet, James-A. - 141-152 Do oil price forecast disagreement of survey of professional forecasters predict crude oil return volatility?
by Hasselgren, Anton & Hou, Ai Jun & Suardi, Sandy & Xu, Caihong & Ye, Xiaoxia - 153-174 Forecasting interest rates with shifting endpoints: The role of the functional demographic age distribution
by Chen, Jiazi & Hong, Zhiwu & Niu, Linlin - 175-190 The time-varying Multivariate Autoregressive Index model
by Cubadda, Gianluca & Grassi, Stefano & Guardabascio, Barbara - 191-207 Boosting domain-specific models with shrinkage: An application in mortality forecasting
by Li, Li & Li, Han & Panagiotelis, Anastasios - 208-228 Predicting the equity premium around the globe: Comprehensive evidence from a large sample
by Hollstein, Fabian & Prokopczuk, Marcel & Tharann, Björn & Wese Simen, Chardin - 229-250 Asymmetric uncertainty: Nowcasting using skewness in real-time data
by Labonne, Paul - 251-269 Dynamic time series modelling and forecasting of COVID-19 in Norway
by Bårdsen, Gunnar & Nymoen, Ragnar - 270-289 ABC-based forecasting in misspecified state space models
by Weerasinghe, Chaya & Loaiza-Maya, Rubén & Martin, Gael M. & Frazier, David T. - 290-306 Multi-view locally weighted regression for loss given default forecasting
by Cheng, Hui & Jiang, Cuiqing & Wang, Zhao & Ni, Xiaoya - 307-320 Forecasting macroeconomic tail risk in real time: Do textual data add value?
by Adämmer, Philipp & Prüser, Jan & Schüssler, Rainer A. - 321-344 Cross-temporal forecast reconciliation at digital platforms with machine learning
by Rombouts, Jeroen & Ternes, Marie & Wilms, Ines - 345-360 A modified VAR-deGARCH model for asynchronous multivariate financial time series via variational Bayesian inference
by Lai, Wei-Ting & Chen, Ray-Bing & Huang, Shih-Feng - 361-376 Sparse time-varying parameter VECMs with an application to modeling electricity prices
by Hauzenberger, Niko & Pfarrhofer, Michael & Rossini, Luca - 377-397 Forecasting realized volatility with spillover effects: Perspectives from graph neural networks
by Zhang, Chao & Pu, Xingyue & Cucuringu, Mihai & Dong, Xiaowen - 398-417 Forecasting house price growth rates with factor models and spatio-temporal clustering
by Mattera, Raffaele & Franses, Philip Hans
2024, Volume 40, Issue 4
- 1275-1301 Forecasting day-ahead expected shortfall on the EUR/USD exchange rate: The (I)relevance of implied volatility
by Lyócsa, Štefan & Plíhal, Tomáš & Výrost, Tomáš - 1302-1335 Nowcasting with panels and alternative data: The OECD weekly tracker
by Woloszko, Nicolas - 1336-1358 Thinking outside the container: A sparse partial least squares approach to forecasting trade flows
by Stamer, Vincent - 1359-1390 Generalized Poisson difference autoregressive processes
by Carallo, Giulia & Casarin, Roberto & Robert, Christian P. - 1391-1409 An assessment of the marginal predictive content of economic uncertainty indexes and business conditions predictors
by Liu, Yang & Swanson, Norman R. - 1410-1420 Forecasting emergency department occupancy with advanced machine learning models and multivariable input
by Tuominen, Jalmari & Pulkkinen, Eetu & Peltonen, Jaakko & Kanniainen, Juho & Oksala, Niku & Palomäki, Ari & Roine, Antti - 1421-1437 A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market
by Cornell, Cameron & Dinh, Nam Trong & Pourmousavi, S. Ali - 1438-1448 Properties of the reconciled distributions for Gaussian and count forecasts
by Zambon, Lorenzo & Agosto, Arianna & Giudici, Paolo & Corani, Giorgio - 1449-1466 CRPS-based online learning for nonlinear probabilistic forecast combination
by van der Meer, Dennis & Pinson, Pierre & Camal, Simon & Kariniotakis, Georges - 1467-1485 Forecasting seasonal demand for retail: A Fourier time-varying grey model
by Ye, Lili & Xie, Naiming & Boylan, John E. & Shang, Zhongju - 1486-1504 Survey density forecast comparison in small samples
by Coroneo, Laura & Iacone, Fabrizio & Profumo, Fabio - 1507-1520 Instance-based meta-learning for conditionally dependent univariate multi-step forecasting
by Cerqueira, Vitor & Torgo, Luis & Bontempi, Gianluca - 1521-1538 Forecasting UK inflation bottom up
by Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George - 1539-1555 Network log-ARCH models for forecasting stock market volatility
by Mattera, Raffaele & Otto, Philipp - 1556-1567 A Bayesian Dirichlet auto-regressive moving average model for forecasting lead times
by Katz, Harrison & Brusch, Kai Thomas & Weiss, Robert E. - 1568-1586 Multivariate probabilistic CRPS learning with an application to day-ahead electricity prices
by Berrisch, Jonathan & Ziel, Florian - 1587-1621 The short-term predictability of returns in order book markets: A deep learning perspective
by Lucchese, Lorenzo & Pakkanen, Mikko S. & Veraart, Almut E.D. - 1622-1645 Improving forecasts for heterogeneous time series by “averaging”, with application to food demand forecasts
by Neubauer, Lukas & Filzmoser, Peter - 1646-1659 Dynamic prediction of the National Hockey League draft with rank-ordered logit models
by Kumagai, Brendan & Moreau, Ryker & Kroetch, Kimberly & Swartz, Tim B. - 1660-1688 Factor-augmented forecasting in big data
by Bae, Juhee - 1689-1700 Hierarchical forecasting at scale
by Sprangers, Olivier & Wadman, Wander & Schelter, Sebastian & de Rijke, Maarten - 1701-1720 Locally tail-scale invariant scoring rules for evaluation of extreme value forecasts
by Olafsdottir, Helga Kristin & Rootzén, Holger & Bolin, David - 1721-1733 A loss discounting framework for model averaging and selection in time series models
by Bernaciak, Dawid & Griffin, Jim E. - 1734-1751 Conditionally optimal weights and forward-looking approaches to combining forecasts
by Gibbs, Christopher G. & Vasnev, Andrey L.
2024, Volume 40, Issue 3
- 859-868 Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties
by Mallen, Alex T. & Lange, Henning & Kutz, J. Nathan - 869-880 A comparison of machine learning methods for predicting the direction of the US stock market on the basis of volatility indices
by Campisi, Giovanni & Muzzioli, Silvia & De Baets, Bernard - 881-902 A False Discovery Rate approach to optimal volatility forecasting model selection
by Hassanniakalager, Arman & Baker, Paul L. & Platanakis, Emmanouil - 903-917 Should I open to forecast? Implications from a multi-country unobserved components model with sparse factor stochastic volatility
by Wu, Ping - 918-941 Comparing forecasting performance with panel data
by Qu, Ritong & Timmermann, Allan & Zhu, Yinchu - 942-957 A multi-task encoder-dual-decoder framework for mixed frequency data prediction
by Lin, Jiahe & Michailidis, George - 958-970 Improving geopolitical forecasts with 100 brains and one computer
by Shinitzky, Hilla & Shemesh, Yhonatan & Leiser, David & Gilead, Michael - 971-984 Network time series forecasting using spectral graph wavelet transform
by Kim, Kyusoon & Oh, Hee-Seok - 985-1001 Systemic bias of IMF reserve and debt forecasts for program countries
by Eicher, Theo S. & Kawai, Reina - 1002-1021 The profitability of lead–lag arbitrage at high frequency
by Poutré, Cédric & Dionne, Georges & Yergeau, Gabriel - 1022-1041 Forecasting crude oil market volatility: A comprehensive look at uncertainty variables
by Wen, Danyan & He, Mengxi & Wang, Yudong & Zhang, Yaojie - 1042-1054 Forecasting euro area inflation using a huge panel of survey expectations
by Huber, Florian & Onorante, Luca & Pfarrhofer, Michael - 1055-1068 Demand forecasting under lost sales stock policies
by Trapero, Juan R. & de Frutos, Enrique Holgado & Pedregal, Diego J. - 1069-1084 A theory-based method to evaluate the impact of central bank inflation forecasts on private inflation expectations
by Vereda, Luciano & Savignon, João & Gouveia da Silva, Tarciso - 1085-1100 Improving models and forecasts after equilibrium-mean shifts
by Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F. - 1101-1122 Evaluating probabilistic classifiers: The triptych
by Dimitriadis, Timo & Gneiting, Tilmann & Jordan, Alexander I. & Vogel, Peter - 1123-1133 DeepTVAR: Deep learning for a time-varying VAR model with extension to integrated VAR
by Li, Xixi & Yuan, Jingsong - 1134-1151 Cross-temporal probabilistic forecast reconciliation: Methodological and practical issues
by Girolimetto, Daniele & Athanasopoulos, George & Di Fonzo, Tommaso & Hyndman, Rob J. - 1152-1165 Rating players by Laplace’s approximation and dynamic modeling
by Hua, Hsuan-Fu & Chang, Ching-Ju & Lin, Tse-Ching & Weng, Ruby Chiu-Hsing - 1166-1178 Out-of-sample predictability in predictive regressions with many predictor candidates
by Gonzalo, Jesús & Pitarakis, Jean-Yves - 1179-1188 Portfolio selection under non-gaussianity and systemic risk: A machine learning based forecasting approach
by Lin, Weidong & Taamouti, Abderrahim - 1189-1205 Short-term stock price trend prediction with imaging high frequency limit order book data
by Ye, Wuyi & Yang, Jinting & Chen, Pengzhan - 1206-1237 Reservoir computing for macroeconomic forecasting with mixed-frequency data
by Ballarin, Giovanni & Dellaportas, Petros & Grigoryeva, Lyudmila & Hirt, Marcel & van Huellen, Sophie & Ortega, Juan-Pablo - 1238-1254 Do professional forecasters believe in the Phillips curve?
by Clements, Michael P. - 1255-1270 Forecasting day-ahead electricity prices with spatial dependence
by Yang, Yifan & Guo, Ju’e & Li, Yi & Zhou, Jiandong
2024, Volume 40, Issue 2
- 430-456 Forecast reconciliation: A review
by Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Panagiotelis, Anastasios - 457-469 Probabilistic reconciliation of count time series
by Corani, Giorgio & Azzimonti, Dario & Rubattu, Nicolò - 470-489 Probabilistic hierarchical forecasting with deep Poisson mixtures
by Olivares, Kin G. & Meetei, O. Nganba & Ma, Ruijun & Reddy, Rohan & Cao, Mengfei & Dicker, Lee - 490-514 Forecast combination-based forecast reconciliation: Insights and extensions
by Di Fonzo, Tommaso & Girolimetto, Daniele - 515-531 Likelihood-based inference in temporal hierarchies
by Møller, Jan Kloppenborg & Nystrup, Peter & Madsen, Henrik - 532-548 Forecasting Australian fertility by age, region, and birthplace
by Yang, Yang & Shang, Han Lin & Raymer, James - 549-563 Hierarchical mortality forecasting with EVT tails: An application to solvency capital requirement
by Li, Han & Chen, Hua - 564-580 Counterfactual reconciliation: Incorporating aggregation constraints for more accurate causal effect estimates
by Cengiz, Doruk & Tekgüç, Hasan