IDEAS home Printed from https://ideas.repec.org/f/c/pli521.html

Feng Li

Not to be confused with: Feng Li

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Bohan Zhang & Yanfei Kang & Anastasios Panagiotelis & Feng Li, 2022. "Optimal reconciliation with immutable forecasts," Papers 2204.09231, arXiv.org.

    Cited by:

    1. Borgonovo, Emanuele & Jose, Victor Richmond R. & Knowlton, Morgan & Shachter, Ross & Siebert, Johannes Ulrich & Ulu, Canan, 2026. "Fifty years of decision analysis in operational research: A review," European Journal of Operational Research, Elsevier, vol. 329(2), pages 355-377.
    2. Daniele Girolimetto & Tommaso Di Fonzo, 2024. "Point and probabilistic forecast reconciliation for general linearly constrained multiple time series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(2), pages 581-607, April.
    3. Zabihinia Gerdroodbari, Yasin & Khorasany, Mohsen & Razzaghi, Reza & Heidari, Rahmat, 2024. "Management of prosumers using dynamic export limits and shared Community Energy Storage," Applied Energy, Elsevier, vol. 355(C).
    4. Abolghasemi, Mahdi & Girolimetto, Daniele & Di Fonzo, Tommaso, 2025. "Improving cross-temporal forecasts reconciliation accuracy and utility in energy market," Applied Energy, Elsevier, vol. 394(C).
    5. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Panagiotelis, Anastasios, 2024. "Forecast reconciliation: A review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 430-456.
    6. Jiang, He & Dong, Yawei & Dong, Yao & Wang, Jianzhou, 2025. "Probabilistic electricity price forecasting by integrating interpretable model," Technological Forecasting and Social Change, Elsevier, vol. 210(C).
    7. Wang, Xiaoqian & Hyndman, Rob J. & Wickramasuriya, Shanika L., 2025. "Optimal forecast reconciliation with time series selection," European Journal of Operational Research, Elsevier, vol. 323(2), pages 455-470.

  2. Li Li & Yanfei Kang & Fotios Petropoulos & Feng Li, 2022. "Feature-based intermittent demand forecast combinations: bias, accuracy and inventory implications," Papers 2204.08283, arXiv.org, revised Aug 2022.

    Cited by:

    1. Schlaich, Tim & Hoberg, Kai, 2024. "When is the next order? Nowcasting channel inventories with Point-of-Sales data to predict the timing of retail orders," European Journal of Operational Research, Elsevier, vol. 315(1), pages 35-49.

  3. Li Li & Yanfei Kang & Feng Li, 2021. "Bayesian forecast combination using time-varying features," Papers 2108.02082, arXiv.org, revised Jun 2022.

    Cited by:

    1. Han Su & Xiaojia Guo & Xiaoke Zhang, 2026. "Regularized Ensemble Forecasting for Learning Weights from Historical and Current Forecasts," Papers 2602.11379, arXiv.org.
    2. Renata Tavanielli & Márcio Laurini, 2023. "Yield Curve Models with Regime Changes: An Analysis for the Brazilian Interest Rate Market," Mathematics, MDPI, vol. 11(11), pages 1-28, June.
    3. Mohammad Naim Azimi & Mohammad Mafizur Rahman & Tek Maraseni, 2026. "Green strategy and ecological efficiency: a nonlinear machine learning analysis under institutional quality intervention," Quality & Quantity: International Journal of Methodology, Springer, vol. 60(1), pages 3075-3128, February.
    4. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    5. Ran Wu & Abdullahi D. Ahmed & Mohammad Zoynul Abedin & Hongjun Zeng, 2026. "HyperVIX: A GWO‐Optimized ARIMA‐LSTM Hybrid Model for CBOE Volatility Index (VIX) Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(1), pages 272-292, January.
    6. Wang, Shengjie & Kang, Yanfei & Petropoulos, Fotios, 2024. "Combining probabilistic forecasts of intermittent demand," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1038-1048.
    7. Tony Chernis & Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2025. "Predictive Density Combination Using Bayesian Machine Learning," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 66(3), pages 1287-1315, August.
    8. Pietro Giorgio Lovaglio, 2025. "Cross‐Learning With Panel Data Modeling for Stacking and Forecast Time Series Employment in Europe," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 753-780, March.
    9. Bernaciak, Dawid & Griffin, Jim E., 2024. "A loss discounting framework for model averaging and selection in time series models," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1721-1733.

  4. 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 & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.

    Cited by:

    1. Nie, Yan & Zhang, Guoxing & Zhong, Luhao & Su, Bin & Xi, Xi, 2024. "Urban‒rural disparities in household energy and electricity consumption under the influence of electricity price reform policies," Energy Policy, Elsevier, vol. 184(C).
    2. Yu Jeffrey Hu & Jeroen Rombouts & Ines Wilms, 2025. "Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms," Information Systems Research, INFORMS, vol. 36(1), pages 552-571, March.
    3. Julia Eichholz & Thorsten Knauer & Sandra Winkelmann, 2023. "Digital Maturity of Forecasting and its Impact in Times of Crisis," Schmalenbach Journal of Business Research, Springer, vol. 75(4), pages 443-481, December.
    4. Said Rosli & Sulaimi Mardhiati & Majid Rohayu Ab & Aini Ainoriza Mohd & Olanrele Olusegun Olaopin & Akinsomi Omokolade, 2024. "Evaluating Market Attributes and Housing Affordability: Gaining Perspective on Future Value Trends," Real Estate Management and Valuation, Sciendo, vol. 32(3), pages 87-100.
    5. Xing, Xiaoxuan & Gong, Dunwei & Wang, Yan & Sun, Xiaoyan & Zhang, Yong, 2025. "Acceptable cost-driven multivariate load forecasting for integrated coal mine energy systems," Applied Energy, Elsevier, vol. 397(C).
    6. Afif Zuhri Muhammad Khodri Harahap & Mohd Kamarul Irwan Abdul Rahim & Noor Malinjasari & Suzila Mat Salleh & Rabiatul Adawiyah Ma'arof, 2025. "Enhancing the Inventory Management through Demand Forecasting," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(1), pages 2737-2744, January.
    7. Pei, Ming & Gong, Ruqing & Ye, Lin & Chen, Lei & Sun, Yihui & Tang, Yong, 2026. "Spatiotemporal sparse autoregressive distributed lag model with extended Regressors for regional wind power forecasting," Applied Energy, Elsevier, vol. 404(C).
    8. Zin Mar Oo & Ching‐Yang Lin & Makoto Kakinaka, 2025. "Deciphering Long‐Term Economic Growth: An Exploration With Leading Machine Learning Techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(4), pages 1531-1562, July.
    9. Martin McCarthy, Stephen Snudden, 2024. "Forecasts of Period-Average Exchange Rates: New Insights from Real-Time Daily Data," LCERPA Working Papers jc0148, Laurier Centre for Economic Research and Policy Analysis, revised Oct 2024.
    10. Raja, Aitazaz Ali & Pinson, Pierre & Kazempour, Jalal & Grammatico, Sergio, 2024. "A market for trading forecasts: A wagering mechanism," International Journal of Forecasting, Elsevier, vol. 40(1), pages 142-159.
    11. Marco Zanotti, 2025. "On the stability of global forecasting models," Working Papers 553, University of Milano-Bicocca, Department of Economics.
    12. Qi Zheng & Yunwei Cui & Rongning Wu, 2024. "On estimation of nonparametric regression models with autoregressive and moving average errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(2), pages 235-262, April.
    13. Karamolegkos, Spyridon & Koulouriotis, Dimitrios E., 2025. "Advancing short-term load forecasting with decomposed Fourier ARIMA: A case study on the Greek energy market," Energy, Elsevier, vol. 325(C).
    14. Simon Hirsch & Jonathan Berrisch & Florian Ziel, 2024. "Online Distributional Regression," Papers 2407.08750, arXiv.org, revised Apr 2026.
    15. Amjad Almusaed & Ibrahim Yitmen & Asaad Almssad, 2023. "Enhancing Smart Home Design with AI Models: A Case Study of Living Spaces Implementation Review," Energies, MDPI, vol. 16(6), pages 1-23, March.
    16. Cheng Zhang, 2026. "A Nontrivial Upper Bound on the Out-of-Sample $R^2$ in Return Forecasting," Papers 2602.07841, arXiv.org, revised Apr 2026.
    17. Jozef Barunik & Lubos Hanus, 2023. "Learning the Probability Distributions of Day-Ahead Electricity Prices," Papers 2310.02867, arXiv.org, revised Jul 2025.
    18. Diego Zappa & Gian Paolo Clemente & Francesco Della Corte & Nino Savelli, 2023. "Editorial on the Special Issue on Insurance: complexity, risks and its connection with social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 125-130, December.
    19. Yi Ding & Peng Wu & Jie Zhao & Ligang Zhou, 2025. "Forecasting product sales using text mining: a case study in new energy vehicle," Electronic Commerce Research, Springer, vol. 25(1), pages 495-527, February.
    20. Ricardo Caetano & José Manuel Oliveira & Patrícia Ramos, 2025. "Transformer-Based Models for Probabilistic Time Series Forecasting with Explanatory Variables," Mathematics, MDPI, vol. 13(5), pages 1-29, February.
    21. Ca’ Zorzi, Michele & Rubaszek, Michał, 2023. "How many fundamentals should we include in the behavioral equilibrium exchange rate model?," Economic Modelling, Elsevier, vol. 118(C).
    22. Kafa, Nadine & Babai, M. Zied & Klibi, Walid, 2025. "Forecasting mail flow: A hierarchical approach for enhanced societal wellbeing," International Journal of Forecasting, Elsevier, vol. 41(1), pages 51-65.
    23. Racek, Daniel & Thurner, Paul W. & Davidson, Brittany I. & Zhu, Xiao Xiang & Kauermann, Göran, 2024. "Conflict forecasting using remote sensing data: An application to the Syrian civil war," International Journal of Forecasting, Elsevier, vol. 40(1), pages 373-391.
    24. Huang, Congzhi & Yang, Mengyuan, 2023. "Memory long and short term time series network for ultra-short-term photovoltaic power forecasting," Energy, Elsevier, vol. 279(C).
    25. Marco Zanotti, 2025. "Do global forecasting models require frequent retraining?," Working Papers 551, University of Milano-Bicocca, Department of Economics.
    26. Cakici, Nusret & Shahzad, Syed Jawad Hussain & Będowska-Sójka, Barbara & Zaremba, Adam, 2024. "Machine learning and the cross-section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 94(C).
    27. Wesley Marcos Almeida & Claudimar Pereira Veiga, 2023. "Does demand forecasting matter to retailing?," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 219-232, June.
    28. Anna Sznajderska & Alfred A. Haug, 2023. "Bayesian VARs of the U.S. economy before and during the pandemic," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(2), pages 211-236, June.
    29. Coleen Tala & Romeo T. Quintos Jr, 2025. "Sipnayan sa Tambakan: Mathematical Ethnomodels Through the Lens of the Scrap Merchants," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(9), pages 3934-3957, September.
    30. Li, Xiaoyuan & Tian, Zhe & Wu, Xia & Feng, Wei & Niu, Jide, 2024. "Optimal planning for hybrid renewable energy systems under limited information based on uncertainty quantification," Renewable Energy, Elsevier, vol. 237(PD).
    31. Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022. "Distributional neural networks for electricity price forecasting," Papers 2207.02832, arXiv.org, revised Dec 2022.
    32. Minghao Ran & Yingchao Wang & Qilu Qin & Jindi Huang & Jiading Jiang, 2025. "An Improved Grey Prediction Model Integrating Periodic Decomposition and Aggregation for Renewable Energy Forecasting: Case Studies of Solar and Wind Power," Sustainability, MDPI, vol. 17(11), pages 1-31, May.
    33. Bernhard Tröster & Ulrich Gunter, 2023. "The Financialization of Coffee, Cocoa and Cotton Value Chains: The Role of Physical Actors," Development and Change, International Institute of Social Studies, vol. 54(6), pages 1550-1574, November.
    34. Tetiana Zatonatska & Olena Liashenko & Yana Fareniuk & Oleksandr Dluhopolskyi & Artur Dmowski & Marzena Cichorzewska, 2022. "The Migration Influence on the Forecasting of Health Care Budget Expenditures in the Direction of Sustainability: Case of Ukraine," Sustainability, MDPI, vol. 14(21), pages 1-17, November.
    35. Chȩć, Katarzyna & Uniejewski, Bartosz & Weron, Rafał, 2025. "Extrapolating the long-term seasonal component of electricity prices for forecasting in the day-ahead market," Journal of Commodity Markets, Elsevier, vol. 37(C).
    36. Marc Wildi, 2026. "Forecasting on the Accuracy-Timeliness Frontier: Two Novel `Look Ahead' Predictors," Papers 2602.23087, arXiv.org.
    37. Singhal, Shakshi & Bano, Yasmeen & Gautam, Prerna, 2026. "Changepoint model for energy-efficient technology diffusion: A comparative evaluation of empirical models," Technovation, Elsevier, vol. 149(C).
    38. Jeroen Rombouts & Marie Ternes & Ines Wilms, 2024. "Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning," Papers 2402.09033, arXiv.org, revised May 2024.
    39. Jonathan Berrisch & Florian Ziel, 2022. "Distributional modeling and forecasting of natural gas prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1065-1086, September.
    40. Oscar Espinosa & Valeria Bejarano & Jeferson Ramos & Boris Martínez, 2023. "Statistical actuarial estimation of the Capitation Payment Unit from copula functions and deep learning: historical comparability analysis for the Colombian health system, 2015–2021," Health Economics Review, Springer, vol. 13(1), pages 1-20, December.
    41. Wang, Lu & Wang, Xing & Liang, Chao, 2024. "Natural gas volatility prediction via a novel combination of GARCH-MIDAS and one-class SVM," The Quarterly Review of Economics and Finance, Elsevier, vol. 98(C).
    42. Meisenbacher, Stefan & Phipps, Kaleb & Taubert, Oskar & Weiel, Marie & Götz, Markus & Mikut, Ralf & Hagenmeyer, Veit, 2025. "AutoPQ: Automating quantile estimation from point forecasts in the context of sustainability," Applied Energy, Elsevier, vol. 392(C).
    43. Shanshan Wang & Shih‐Chih Chen & Mohd Helmi Ali & Ming‐Lang Tseng, 2024. "Nexus of environmental, social, and governance performance in China‐listed companies: Disclosure and green bond issuance," Business Strategy and the Environment, Wiley Blackwell, vol. 33(3), pages 1647-1660, March.
    44. Alroomi, Azzam & Karamatzanis, Georgios & Nikolopoulos, Konstantinos & Tilba, Anna & Xiao, Shujun, 2022. "Fathoming empirical forecasting competitions’ winners," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1519-1525.
    45. Rai, Amit & Shrivastava, Ashish & Jana, Kartick C., 2023. "Differential attention net: Multi-directed differential attention based hybrid deep learning model for solar power forecasting," Energy, Elsevier, vol. 263(PC).
    46. Marco Zanotti, 2025. "The cost of ensembling: is it always worth combining?," Working Papers 554, University of Milano-Bicocca, Department of Economics.
    47. Anita M. Bunea & Mariangela Guidolin & Piero Manfredi & Pompeo Della Posta, 2022. "Diffusion of Solar PV Energy in the UK: A Comparison of Sectoral Patterns," Forecasting, MDPI, vol. 4(2), pages 1-21, April.
    48. Zheng, Zhuang & Shafique, Muhammad & Luo, Xiaowei & Wang, Shengwei, 2024. "A systematic review towards integrative energy management of smart grids and urban energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    49. Fernández, Joaquín Delgado & Menci, Sergio Potenciano & Lee, Chul Min & Rieger, Alexander & Fridgen, Gilbert, 2022. "Privacy-preserving federated learning for residential short-term load forecasting," Applied Energy, Elsevier, vol. 326(C).
    50. Lesia Korolchuk, 2023. "Application of forecasting methods in harmonising strategic planning for sustainable development of the state," E-Forum Working Papers, Economic Forum, vol. 14(1), pages 50-61, December.
    51. Singhal, Shakshi & Bano, Yasmeen & Singh, Ompal, 2025. "Investigating the role of customer's disadoption and dynamic shifts in mobile cellular diffusion: Evidence from emerging economies," Technological Forecasting and Social Change, Elsevier, vol. 219(C).
    52. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2023. "Modeling and forecasting dynamic conditional correlations with opening, high, low, and closing prices," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 308-321.
    53. Li, Xin & Xu, Yechi & Law, Rob & Wang, Shouyang, 2024. "Enhancing tourism demand forecasting with a transformer-based framework," Annals of Tourism Research, Elsevier, vol. 107(C).
    54. Paul Ghelasi & Florian Ziel, 2023. "Hierarchical forecasting for aggregated curves with an application to day-ahead electricity price auctions," Papers 2305.16255, arXiv.org.
    55. Augusto Cerqua & Marco Letta & Gabriele Pinto, 2024. "On the (Mis)Use of Machine Learning with Panel Data," Papers 2411.09218, arXiv.org, revised May 2025.
    56. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.
    57. Allen, Sam & Koh, Jonathan & Segers, Johan & Ziegel, Johanna, 2024. "Tail calibration of probabilistic forecasts," LIDAM Discussion Papers ISBA 2024018, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    58. Janczura, Joanna & Wójcik, Edyta, 2022. "Dynamic short-term risk management strategies for the choice of electricity market based on probabilistic forecasts of profit and risk measures. The German and the Polish market case study," Energy Economics, Elsevier, vol. 110(C).
    59. Heymann, Fabian & Milojevic, Tatjana & Covatariu, Andrei & Verma, Piyush, 2023. "Digitalization in decarbonizing electricity systems – Phenomena, regional aspects, stakeholders, use cases, challenges and policy options," Energy, Elsevier, vol. 262(PB).
    60. Spiliotis, Evangelos & Petropoulos, Fotios, 2024. "On the update frequency of univariate forecasting models," European Journal of Operational Research, Elsevier, vol. 314(1), pages 111-121.
    61. Cakici, Nusret & Zaremba, Adam, 2025. "Accounting vs technical information: what matters more for stock return predictability?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 104(C).
    62. Entezari, Negin & Fuinhas, José Alberto, 2024. "Measuring wholesale electricity price risk from climate change: Evidence from Portugal," Utilities Policy, Elsevier, vol. 91(C).
    63. Chen Chuanglian & Lin Huanheng & Lin Yuting, 2026. "Deep Learning‐Based Network Relationship Construction Method and Its Impact on Futures Risk Premiums," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 46(1), pages 175-196, January.
    64. Ramos, Paulo Vitor B. & Villela, Saulo Moraes & Silva, Walquiria N. & Dias, Bruno H., 2023. "Residential energy consumption forecasting using deep learning models," Applied Energy, Elsevier, vol. 350(C).
    65. Ghelasi, Paul & Ziel, Florian, 2024. "Hierarchical forecasting for aggregated curves with an application to day-ahead electricity price auctions," International Journal of Forecasting, Elsevier, vol. 40(2), pages 581-596.
    66. Samartzis, Panagiotis, 2025. "Predicting the relative performance among financial assets: A comparative analysis of different approaches," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1428-1449.
    67. Pedersen, Michael, 2025. "Judgment in macroeconomic output growth predictions: Efficiency, accuracy and persistence," International Journal of Forecasting, Elsevier, vol. 41(2), pages 475-486.
    68. Jacek Batóg & Barbara Batóg & Magdalena Mojsiewicz & Przemysław Pluskota, 2024. "Electrification of Public Urban Transport: Funding Opportunities, Bus Fleet, and Energy Use Forecasts for Poland," Energies, MDPI, vol. 17(23), pages 1-20, December.
    69. Katarzyna Chec & Bartosz Uniejewski & Rafal Weron, 2026. "From biased point forecasts of electricity demand to accurate predictive distributions: Using LASSO and GAMLSS," WORking papers in Management Science (WORMS) WORMS/26/01, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    70. Mascarenhas, Maria Margarida & De Blauwe, Jilles & Amelin, Mikael & Kazmi, Hussain, 2026. "Leveraging asynchronous cross-border market data for improved day-ahead electricity price forecasting in European markets," Applied Energy, Elsevier, vol. 404(C).
    71. Wang, Shengjie & Kang, Yanfei & Petropoulos, Fotios, 2024. "Combining probabilistic forecasts of intermittent demand," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1038-1048.
    72. Caravaggio, Nicola & Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2025. "Predicting policy funding allocation with Machine Learning," Socio-Economic Planning Sciences, Elsevier, vol. 98(C).
    73. Victoria A Bensel & Kelsey Corcoran & Anthony J Lisi, 2025. "Forecasting the use of chiropractic services within the Veterans Health Administration," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-8, January.
    74. Katarzyna Maciejowska & Weronika Nitka, 2024. "Multiple split approach -- multidimensional probabilistic forecasting of electricity markets," Papers 2407.07795, arXiv.org.
    75. Divya Aggarwal & Sougata Banerjee, 2025. "Forecasting of S&P 500 ESG Index by Using CEEMDAN and LSTM Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 339-355, March.
    76. Bergsteinsson, Hjörleifur G. & Sørensen, Mikkel Lindstrøm & Møller, Jan Kloppenborg & Madsen, Henrik, 2023. "Heat load forecasting using adaptive spatial hierarchies," Applied Energy, Elsevier, vol. 350(C).
    77. Malte C. Tichy & Illia Babounikau & Nikolas Wolke & Stefan Ulbrich & Michael Feindt, 2026. "Scaling‐Aware Rating of Poisson‐Limited Demand Forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(2), pages 787-805, March.
    78. Lv, Yichen & Gao, Mingyun & Xiao, Xinping, 2026. "Unbiased forecasting of seasonal wind power generation based on a novel seasonal multivariable grey model," Renewable Energy, Elsevier, vol. 258(C).
    79. Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022. "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers 2209.09649, arXiv.org, revised Jul 2023.
    80. Mutele, Litshedzani & Carranza, Emmanuel John M., 2024. "Statistical analysis of gold production in South Africa using ARIMA, VAR and ARNN modelling techniques: Extrapolating future gold production, Resources–Reserves depletion, and Implication on South Africa's gold exploration," Resources Policy, Elsevier, vol. 93(C).
    81. Takahashi, Carlos Kazunari & Figueiredo, Júlio César Bastos de & Scornavacca, Eusebio, 2024. "Investigating the diffusion of innovation: A comprehensive study of successive diffusion processes through analysis of search trends, patent records, and academic publications," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    82. Li, Xishu & Zuidwijk, Rob & de Koster, M.B.M, 2023. "Optimal competitive capacity strategies: Evidence from the container shipping market," Omega, Elsevier, vol. 115(C).
    83. Pan Tang & Yuwei Zhang, 2024. "China's business cycle forecasting: a machine learning approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(5), pages 2783-2811, November.
    84. Shi, Qi, 2025. "Technical indicators and aggregate stock returns: An updated look," Journal of Multinational Financial Management, Elsevier, vol. 77(C).
    85. Richard Bean, 2023. "Forecasting the Monash Microgrid for the IEEE-CIS Technical Challenge," Energies, MDPI, vol. 16(3), pages 1-23, January.
    86. Alex Tschan & Lars Hetzel & Ralf Eisinger & Carolin Eggen & Claudia Heuser & Victoria Fritz, 2025. "Data-Driven Inventory Control and Integrated Employee Involvement for Special Buys at ALDI SÜD Germany," Interfaces, INFORMS, vol. 55(1), pages 22-35, January.
    87. Shafie Bahman & Hamidreza Zareipour, 2025. "Long-Term Multi-Resolution Probabilistic Load Forecasting Using Temporal Hierarchies," Energies, MDPI, vol. 18(11), pages 1-30, June.
    88. Emmanuel Senyo Fianu, 2022. "Analyzing and Forecasting Multi-Commodity Prices Using Variants of Mode Decomposition-Based Extreme Learning Machine Hybridization Approach," Forecasting, MDPI, vol. 4(2), pages 1-27, June.
    89. Safdar, Muhammad & Zhong, Ming & Ren, Zhi & Li, Linfeng & Raza, Asif & Hunt, John Douglas, 2026. "An integrated spatial economic modeling framework for forecasting inland waterway freight demand," Transport Policy, Elsevier, vol. 176(C).
    90. Mingzhe Shi & Bahman Rostami-Tabar & Daniel Gartner, 2025. "Looking for the crystal ball in unscheduled care: a systematic literature review of the forecasting process," Health Care Management Science, Springer, vol. 28(3), pages 548-564, September.
    91. Cristiana Tudor & Robert Sova, 2025. "An automated adaptive trading system for enhanced performance of emerging market portfolios," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-39, December.
    92. Joanna Janczura & Andrzej Puć, 2023. "ARX-GARCH Probabilistic Price Forecasts for Diversification of Trade in Electricity Markets—Variance Stabilizing Transformation and Financial Risk-Minimizing Portfolio Allocation," Energies, MDPI, vol. 16(2), pages 1-28, January.
    93. Elalem, Yara Kayyali & Maier, Sebastian & Seifert, Ralf W., 2023. "A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1874-1894.
    94. Brown, David P. & Cajueiro, Daniel O. & Eckert, Andrew & Silveira, Douglas, 2025. "Evaluating the role of information disclosure on bidding behavior in wholesale electricity markets," Energy Economics, Elsevier, vol. 146(C).
    95. Theodorou, Evangelos & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2025. "Forecast accuracy and inventory performance: Insights on their relationship from the M5 competition data," European Journal of Operational Research, Elsevier, vol. 322(2), pages 414-426.
    96. Jun Meng & Jingfang Fan & Uma S. Bhatt & Jürgen Kurths, 2023. "Arctic weather variability and connectivity," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    97. Emanuela Raffinetti, 2023. "A Rank Graduation Accuracy measure to mitigate Artificial Intelligence risks," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 131-150, December.
    98. Marta Crispino & Vincenzo Mariani, 2025. "A Tool to Nowcast Tourist Overnight Stays with Payment Data and Complementary Indicators," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 11(1), pages 285-312, March.
    99. Aitazaz Ali Raja & Pierre Pinson & Jalal Kazempour & Sergio Grammatico, 2022. "A Market for Trading Forecasts: A Wagering Mechanism," Papers 2205.02668, arXiv.org, revised Oct 2022.
    100. Conigliani, Caterina & Costantini, Valeria & Paglialunga, Elena & Tancredi, Andrea, 2024. "Forecasting the climate-conflict risk in Africa along climate-related scenarios and multiple socio-economic drivers," Economic Modelling, Elsevier, vol. 141(C).
    101. Wang, Xiaoqian & Kang, Yanfei & Hyndman, Rob J. & Li, Feng, 2023. "Distributed ARIMA models for ultra-long time series," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1163-1184.
    102. Katarzyna Chk{e}'c & Bartosz Uniejewski & Rafa{l} Weron, 2025. "Extrapolating the long-term seasonal component of electricity prices for forecasting in the day-ahead market," Papers 2503.02518, arXiv.org.
    103. Robinson Kruse‐Becher, 2025. "Adaptive Now‐ and Forecasting of Global Temperatures Under Smooth Structural Changes," Environmetrics, John Wiley & Sons, Ltd., vol. 36(6), September.
    104. Abdelfatah, Omar Sharafeldin Mohamed, 2026. "Machine Learning Approaches for Improving Demand Forecasting Accuracy in Retail Supply Chains," SocArXiv 4z9be_v1, Center for Open Science.
    105. Bogdan Oancea & Mihaela Simionescu & Richard Pospisil, 2025. "Do Machine Learning Techniques Outperform Autoregressive Distributed Lag Models in Inflation Forecasting?," Prague Economic Papers, Prague University of Economics and Business, vol. 2025(4), pages 495-558.
    106. Filipe R. Ramos & Luisa M. Martinez & Luis F. Martinez & Ricardo Abreu & Lihki Rubio, 2025. "Mapping e-commerce trends in the USA: a time series and deep learning approach," Journal of Marketing Analytics, Palgrave Macmillan, vol. 13(3), pages 606-634, September.
    107. An, Min Jeong & Jung, Seung Hwan & Lee, Dong Hee, 2025. "Demand forecasting in micro-fulfillment centers using association rule-based machine learning," International Journal of Production Economics, Elsevier, vol. 290(C).
    108. Agakishiev, Ilyas & Härdle, Wolfgang Karl & Kopa, Milos & Kozmik, Karel & Petukhina, Alla, 2025. "Multivariate probabilistic forecasting of electricity prices with trading applications," Energy Economics, Elsevier, vol. 141(C).
    109. Paul Ghelasi & Florian Ziel, 2025. "A data-driven merit order: Learning a fundamental electricity price model," Papers 2501.02963, arXiv.org, revised Nov 2025.
    110. Niklas Valentin Lehmann, 2023. "Forecasting skill of a crowd-prediction platform: A comparison of exchange rate forecasts," Papers 2312.09081, arXiv.org, revised May 2025.
    111. Silvia Golia & Luigi Grossi & Matteo Pelagatti, 2022. "Machine Learning Models and Intra-Daily Market Information for the Prediction of Italian Electricity Prices," Forecasting, MDPI, vol. 5(1), pages 1-21, December.
    112. Li, Xin & Xu, Yechi & Law, Rob & Wang, Shouyang, 2024. "Enhancing Tourism Demand Forecasting with a Transformer-based Framework," SocArXiv 5ezn3_v1, Center for Open Science.
    113. Fałdziński, Marcin & Fiszeder, Piotr & Molnár, Peter, 2024. "Improving volatility forecasts: Evidence from range-based models," The North American Journal of Economics and Finance, Elsevier, vol. 69(PB).
    114. Qi, Lingzhi & Li, Xixi & Wang, Qiang & Jia, Suling, 2023. "fETSmcs: Feature-based ETS model component selection," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1303-1317.
    115. Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2022. "Common factors and the dynamics of cereal prices. A forecasting perspective," Journal of Commodity Markets, Elsevier, vol. 28(C).
    116. Guo, Su & Zheng, Kun & He, Yi & Kurban, Aynur, 2023. "The artificial intelligence-assisted short-term optimal scheduling of a cascade hydro-photovoltaic complementary system with hybrid time steps," Renewable Energy, Elsevier, vol. 202(C), pages 1169-1189.
    117. Walaa M. Rezk, 2025. "The Impact of Digital Economy Development on Total Factor Productivity in Saudi Arabia: A Panel Data Analysis," SAGE Open, , vol. 15(4), pages 21582440251, November.
    118. Swaminathan, Kritika & Venkitasubramony, Rakesh, 2024. "Demand forecasting for fashion products: A systematic review," International Journal of Forecasting, Elsevier, vol. 40(1), pages 247-267.
    119. Andrea Savio & Luigi De Giovanni & Mariangela Guidolin, 2022. "Modelling Energy Transition in Germany: An Analysis through Ordinary Differential Equations and System Dynamics," Forecasting, MDPI, vol. 4(2), pages 1-18, April.
    120. Radovan Šomplák & Veronika Smejkalová & Martin Rosecký & Lenka Szásziová & Vlastimír Nevrlý & Dušan Hrabec & Martin Pavlas, 2023. "Comprehensive Review on Waste Generation Modeling," Sustainability, MDPI, vol. 15(4), pages 1-29, February.
    121. Ye, Lili & Xie, Naiming & Boylan, John E. & Shang, Zhongju, 2024. "Forecasting seasonal demand for retail: A Fourier time-varying grey model," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1467-1485.
    122. Pierre Pinson & Mikkel Bjørn & Simon Kristiansen & Claus B. Nielsen & Lasse Janerka & Jesper Skovgaard & Kristian Durhuus, 2025. "Data-Driven at Sea: Forecasting and Revenue Management at Molslinjen," Interfaces, INFORMS, vol. 55(1), pages 5-21, January.

  5. Xiaoqian Wang & Yanfei Kang & Rob J Hyndman & Feng Li, 2020. "Distributed ARIMA Models for Ultra-long Time Series," Monash Econometrics and Business Statistics Working Papers 29/20, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 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 & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Sommerfeldt, Nelson & Pearce, Joshua M., 2023. "Can grid-tied solar photovoltaics lead to residential heating electrification? A techno-economic case study in the midwestern U.S," Applied Energy, Elsevier, vol. 336(C).
    3. Shen, Dongxu & Yang, Dazhi & Lyu, Chao & Ma, Jingyan & Hinds, Gareth & Sun, Qingmin & Du, Limei & Wang, Lixin, 2024. "Multi-sensor multi-mode fault diagnosis for lithium-ion battery packs with time series and discriminative features," Energy, Elsevier, vol. 290(C).
    4. Xun Dou & Yu He, 2025. "A Short-Term Electricity Load Complementary Forecasting Method Based on Bi-Level Decomposition and Complexity Analysis," Mathematics, MDPI, vol. 13(7), pages 1-22, March.
    5. Ramsebner, J. & Haas, R. & Auer, H. & Ajanovic, A. & Gawlik, W. & Maier, C. & Nemec-Begluk, S. & Nacht, T. & Puchegger, M., 2021. "From single to multi-energy and hybrid grids: Historic growth and future vision," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    6. Islam, M.S. & Das, Barun K. & Das, Pronob & Rahaman, Md Habibur, 2021. "Techno-economic optimization of a zero emission energy system for a coastal community in Newfoundland, Canada," Energy, Elsevier, vol. 220(C).
    7. Haojie Liu & Zihan Lin, 2025. "A Projection-Based ARIMA Framework for Nonlinear Dynamics in Macroeconomic and Financial Time Series: Closed-Form Estimation and Rolling-Window Inference," Papers 2507.07469, arXiv.org, revised Mar 2026.
    8. Feng, Lingbing & Zheng, Yuhao & Wang, Xinyi & Guo, Chuan & Xue, Rui, 2025. "Global stock market forecasting: Insights from series and parallel combination of machine learning models," Pacific-Basin Finance Journal, Elsevier, vol. 93(C).
    9. Şenol, Halil & Çolak, Emre & Oda, Volkan, 2024. "Forecasting of biogas potential using artificial neural networks and time series models for Türkiye to 2035," Energy, Elsevier, vol. 302(C).

  6. Thiyanga S. Talagala & Feng Li & Yanfei Kang, 2019. "Feature-based Forecast-Model Performance Prediction," Monash Econometrics and Business Statistics Working Papers 21/19, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Li Li & Yanfei Kang & Feng Li, 2021. "Bayesian forecast combination using time-varying features," Papers 2108.02082, arXiv.org, revised Jun 2022.

  7. Yanfei Kang & Rob J Hyndman & Feng Li, 2018. "Efficient generation of time series with diverse and controllable characteristics," Monash Econometrics and Business Statistics Working Papers 15/18, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Spiliotis, Evangelos & Kouloumos, Andreas & Assimakopoulos, Vassilios & Makridakis, Spyros, 2020. "Are forecasting competitions data representative of the reality?," International Journal of Forecasting, Elsevier, vol. 36(1), pages 37-53.

  8. Li, Feng & Villani, Mattias & Kohn, Robert, 2010. "Modeling Conditional Densities Using Finite Smooth Mixtures," Working Paper Series 245, Sveriges Riksbank (Central Bank of Sweden).

    Cited by:

    1. Tsionas, Mike, 2012. "Simple techniques for likelihood analysis of univariate and multivariate stable distributions: with extensions to multivariate stochastic volatility and dynamic factor models," MPRA Paper 40966, University Library of Munich, Germany, revised 20 Aug 2012.
    2. Almeida e Santos Nogueira, R.J. & Basturk, N. & Kaymak, U. & Costa Sousa, J.M., 2013. "Estimation of flexible fuzzy GARCH models for conditional density estimation," ERIM Report Series Research in Management ERS-2013-013-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    3. Villani, Mattias & Kohn, Robert & Nott, David J., 2012. "Generalized smooth finite mixtures," Journal of Econometrics, Elsevier, vol. 171(2), pages 121-133.
    4. Yanfei Kang & Rob J Hyndman & Feng Li, 2018. "Efficient generation of time series with diverse and controllable characteristics," Monash Econometrics and Business Statistics Working Papers 15/18, Monash University, Department of Econometrics and Business Statistics.

Articles

  1. Zhang, Bohan & Kang, Yanfei & Panagiotelis, Anastasios & Li, Feng, 2023. "Optimal reconciliation with immutable forecasts," European Journal of Operational Research, Elsevier, vol. 308(2), pages 650-660.
    See citations under working paper version above.
  2. Wang, Xiaoqian & Kang, Yanfei & Hyndman, Rob J. & Li, Feng, 2023. "Distributed ARIMA models for ultra-long time series," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1163-1184.
    See citations under working paper version above.
  3. Li, Li & Kang, Yanfei & Li, Feng, 2023. "Bayesian forecast combination using time-varying features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1287-1302.
    See citations under working paper version above.
  4. Li Li & Yanfei Kang & Fotios Petropoulos & Feng Li, 2023. "Feature-based intermittent demand forecast combinations: accuracy and inventory implications," International Journal of Production Research, Taylor & Francis Journals, vol. 61(22), pages 7557-7572, November.

    Cited by:

    1. Zhang, Bohan & Panagiotelis, Anastasios & Li, Han, 2025. "Constructing hierarchical time series through clustering: Is there an optimal way for forecasting?," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1022-1036.

  5. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.

    Cited by:

    1. Stratigakos, Akylas & Pineda, Salvador & Morales, Juan Miguel, 2025. "Decision-focused linear pooling for probabilistic forecast combination," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1112-1125.
    2. Jinjun Liu & Ming-Yen Cheng, 2026. "Forecasting the U.S. Treasury Yield Curve: A Distributionally Robust Machine Learning Approach," Papers 2601.04608, arXiv.org.
    3. Marco Zanotti, 2025. "On the stability of global forecasting models," Working Papers 553, University of Milano-Bicocca, Department of Economics.
    4. Taylor, James W., 2026. "Probabilistic forecast aggregation with statistical depth," European Journal of Operational Research, Elsevier, vol. 328(2), pages 460-476.
    5. Kozyrev, Boris, 2024. "Forecast combination and interpretability using random subspace," IWH Discussion Papers 21/2024, Halle Institute for Economic Research (IWH).
    6. Alexandra Bozhechkova & Urmat Dzhunkeev, 2024. "CLARA and CARLSON: Combination of Ensemble and Neural Network Machine Learning Methods for GDP Forecasting," Russian Journal of Money and Finance, Bank of Russia, vol. 83(3), pages 45-69, September.
    7. Lin, Tzu-Chi & Liu, Chu-An, 2025. "Model averaging prediction for possibly nonstationary autoregressions," Journal of Econometrics, Elsevier, vol. 249(PB).
    8. Giovanni Ballarin & Lyudmila Grigoryeva & Yui Ching Li, 2025. "From Many Models, One: Macroeconomic Forecasting with Reservoir Ensembles," Papers 2512.13642, arXiv.org, revised Jan 2026.
    9. Wu, Haoran & Gao, Zhiwei & Nie, Boyang & Zhao, Binru, 2025. "Can machines learn Chinese mutual funds?," Pacific-Basin Finance Journal, Elsevier, vol. 94(C).
    10. Lachana, Ioanna & Schröder, David, 2025. "Investor sentiment and stock returns: Wisdom of crowds or power of words? Evidence from Seeking Alpha and Wall Street Journal," Journal of Financial Markets, Elsevier, vol. 74(C).
    11. Chen, Yi-Ting & Liu, Chu-An & Su, Jiun-Hua, 2025. "Bregman model averaging for forecast combination," Journal of Econometrics, Elsevier, vol. 251(C).
    12. Vincenzo Candila & Oguzhan Cepni & Giampiero M. Gallo & Rangan Gupta, 2024. "Influence of Local and Global Economic Policy Uncertainty on the Volatility of US State-Level Equity Returns: Evidence from a GARCH-MIDAS Approach with Shrinkage and Cluster Analysis," Working Papers 202437, University of Pretoria, Department of Economics.
    13. Foucault, Thierry & Gambacorta, Leonardo & Jiang, Wei & Vives, Xavier, 2025. "Barcelona 7: Artificial Intelligence in Finance," HEC Research Papers Series 1599, HEC Paris.
    14. Thompson, Ryan & Qian, Yilin & Vasnev, Andrey L., 2024. "Flexible global forecast combinations," Omega, Elsevier, vol. 126(C).
    15. Li, Bowen & Ampah, Jeffrey Dankwa & Li, Tiantian & Zhang, Xing & Liu, Haifeng & Feng, Hongqing & Yue, Zongyu & Hussain Ratlamwala, Tahir Abdul & Yao, Mingfa, 2025. "Enhancing renewable energy load forecasting through deep data analysis and feature extraction techniques," Energy, Elsevier, vol. 340(C).
    16. Vasiliki Skintzi & Stavroula P. Fameliti, 2025. "Combining realized volatility estimators based on economic performance," Journal of Asset Management, Palgrave Macmillan, vol. 26(7), pages 819-846, December.
    17. Cao, Chaojin & He, Yaoyao & Zhou, Yue & Wang, Shuo, 2025. "An online probabilistic combination framework for power load forecasting under concept-drifting scenarios," Applied Energy, Elsevier, vol. 399(C).
    18. Jeff Tayman & David A. Swanson, 2025. "A Simplified Version of the Hamilton–Perry Method for Forecasting Population by Age Group and Gender," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 44(3), pages 1-33, June.
    19. Han Su & Xiaojia Guo & Xiaoke Zhang, 2026. "Regularized Ensemble Forecasting for Learning Weights from Historical and Current Forecasts," Papers 2602.11379, arXiv.org.
    20. Koo, Moon Su & Lee, Yun Shin & Seifert, Matthias, 2025. "Investigating laypeople’s short- and long-term forecasts of COVID-19 infection cycles," International Journal of Forecasting, Elsevier, vol. 41(2), pages 452-465.
    21. James W. Taylor & Chao Wang, 2025. "Combining a Large Pool of Forecasts of Value-at-Risk and Expected Shortfall," Papers 2508.16919, arXiv.org, revised May 2026.
    22. Faria, Gonçalo & Verona, Fabio, 2024. "Enhancing forecast accuracy through frequencydomain combination: Applications to financial and economic indicators," Bank of Finland Research Discussion Papers 14/2024, Bank of Finland.
    23. Marco Zanotti, 2025. "The cost of ensembling: is it always worth combining?," Working Papers 554, University of Milano-Bicocca, Department of Economics.
    24. Ekaterina Astafyeva & Marina Turuntseva, 2024. "Forecast evaluation improving using the simplest methods of individual forecasts’ combination," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 74, pages 78-103.
    25. Philippe Goulet Coulombe & Maximilian Goebel & Karin Klieber, 2024. "Dual Interpretation of Machine Learning Forecasts," Papers 2412.13076, arXiv.org.
    26. Philippe Goulet Coulombe & Karin Klieber & Christophe Barrette & Maximilian Goebel, 2024. "Maximally Forward-Looking Core Inflation," Papers 2404.05209, arXiv.org.
    27. Daniil Koloskov & Marina Turuntseva, 2025. "The oil and coke prices forecast evaluation using the different forecasting scheme," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 80, pages 5-25.
    28. Pedersen, Michael, 2025. "Judgment in macroeconomic output growth predictions: Efficiency, accuracy and persistence," International Journal of Forecasting, Elsevier, vol. 41(2), pages 475-486.
    29. Dengao Li & Qi Liu & Ding Feng & Zhichao Chen, 2024. "A Medium- and Long-Term Residential Load Forecasting Method Based on Discrete Cosine Transform-FEDformer," Energies, MDPI, vol. 17(15), pages 1-14, July.
    30. Li, Zhao-Chen & Xie, Chi & Wang, Gang-Jin & Zhu, You & Zeng, Zhi-Jian & Gong, Jue, 2024. "Forecasting global stock market volatilities: A shrinkage heterogeneous autoregressive (HAR) model with a large cross-market predictor set," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 673-711.
    31. Wang, Shengjie & Kang, Yanfei & Petropoulos, Fotios, 2024. "Combining probabilistic forecasts of intermittent demand," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1038-1048.
    32. Gabe J. Bondt, 2025. "Future real GDP: real interest rate and inflation matter," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 49(3), pages 661-681, September.
    33. Yu, Dalei & Tang, Nian-Sheng & Shi, Yang, 2025. "Adaptively aggregated forecast for exponential family panel model," International Journal of Forecasting, Elsevier, vol. 41(2), pages 733-747.
    34. Brusaferri, Alessandro & Ballarino, Andrea & Grossi, Luigi & Laurini, Fabrizio, 2025. "On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity prices," Applied Energy, Elsevier, vol. 398(C).
    35. Kohút, Roman & Klaučo, Martin & Kvasnica, Michal, 2025. "Unified carbon emissions and market prices forecasts of the power grid," Applied Energy, Elsevier, vol. 377(PC).
    36. Nathan Canen & Kyungchul Song, 2025. "Simple Inference on a Simplex-Valued Weight," Papers 2501.15692, arXiv.org, revised Jan 2026.
    37. Zhang, Bohan & Panagiotelis, Anastasios & Li, Han, 2025. "Constructing hierarchical time series through clustering: Is there an optimal way for forecasting?," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1022-1036.
    38. Coqueret, Guillaume & Pérignon, Christophe, 2025. "Persistent Anomalies and Nonstandard Errors," HEC Research Papers Series 1578, HEC Paris.
    39. Zhu, Ziyang & Zheng, Yuhao & Wang, Xinyi & Huang, Dasen & Feng, Lingbing, 2025. "Forecasting China's precious metal futures volatility: GBRT models and time-model dimension combination of Tree SHAP," International Review of Financial Analysis, Elsevier, vol. 104(PA).
    40. Bernaciak, Dawid & Griffin, Jim E., 2024. "A loss discounting framework for model averaging and selection in time series models," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1721-1733.
    41. Sonnleitner, Benedikt & Kourentzes, Nikolaos & Ehrig, Claudia & Pflaum, Alexander, 2025. "Forecasting for optimization in road freight transport: A review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 204(C).

  6. Talagala, Thiyanga S. & Li, Feng & Kang, Yanfei, 2022. "FFORMPP: Feature-based forecast model performance prediction," International Journal of Forecasting, Elsevier, vol. 38(3), pages 920-943.

    Cited by:

    1. Li, Li & Kang, Yanfei & Li, Feng, 2023. "Bayesian forecast combination using time-varying features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1287-1302.
    2. Spiliotis, Evangelos & Petropoulos, Fotios, 2024. "On the update frequency of univariate forecasting models," European Journal of Operational Research, Elsevier, vol. 314(1), pages 111-121.
    3. Srivastava, Mahima & Tiwari, Prashant Kumar, 2024. "A profit driven optimal scheduling of virtual power plants for peak load demand in competitive electricity markets with machine learning based forecasted generations," Energy, Elsevier, vol. 310(C).

  7. Kang, Yanfei & Cao, Wei & Petropoulos, Fotios & Li, Feng, 2022. "Forecast with forecasts: Diversity matters," European Journal of Operational Research, Elsevier, vol. 301(1), pages 180-190.

    Cited by:

    1. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    2. Huosong Xia & Xiaoyu Hou & Justin Zuopeng Zhang & Mohammad Zoynul Abedin, 2025. "A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 112-135, January.
    3. Wu, Haoran & Gao, Zhiwei & Nie, Boyang & Zhao, Binru, 2025. "Can machines learn Chinese mutual funds?," Pacific-Basin Finance Journal, Elsevier, vol. 94(C).
    4. Mishra, Abhinay & Kundu, Tanmoy & Kapoor, Rohit & Goh, Mark, 2025. "Blockchain adoption in cross-border cold supply chains: Cost, Efficiency and Trust," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 201(C).
    5. James W. Taylor & Chao Wang, 2025. "Combining a Large Pool of Forecasts of Value-at-Risk and Expected Shortfall," Papers 2508.16919, arXiv.org, revised May 2026.
    6. Liu, Chi & Xu, Zhezhuang & Yuan, Meng & Xie, Junwei & Yuan, Yazhou & Ma, Kai, 2025. "Building electrical load forecasting with occupancy data based on wireless sensing," Applied Energy, Elsevier, vol. 380(C).
    7. Diss, Mostapha & Gassi, Clinton Gubong & Kamwa, Eric, 2026. "On the price of diversity for multiwinner elections under (weakly) separable scoring rules," European Journal of Operational Research, Elsevier, vol. 328(1), pages 258-268.
    8. Xidonas, Panos & Thomakos, Dimitris & Samitas, Aristeidis, 2025. "On the integration of multiple criteria decision aiding and forecasting: Does it create value in portfolio selection?," European Journal of Operational Research, Elsevier, vol. 321(2), pages 516-528.

  8. Xiaoqian Wang & Yanfei Kang & Fotios Petropoulos & Feng Li, 2022. "The uncertainty estimation of feature-based forecast combinations," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(5), pages 979-993, May.

    Cited by:

    1. Magnus, Jan R. & Vasnev, Andrey L., 2023. "On the uncertainty of a combined forecast: The critical role of correlation," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1895-1908.
    2. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    3. Wang, Xiaoqian & Kang, Yanfei & Hyndman, Rob J. & Li, Feng, 2023. "Distributed ARIMA models for ultra-long time series," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1163-1184.
    4. Zhang, Bohan & Panagiotelis, Anastasios & Li, Han, 2025. "Constructing hierarchical time series through clustering: Is there an optimal way for forecasting?," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1022-1036.
    5. Pietro Giorgio Lovaglio, 2025. "Cross‐Learning With Panel Data Modeling for Stacking and Forecast Time Series Employment in Europe," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 753-780, March.

  9. Anderer, Matthias & Li, Feng, 2022. "Hierarchical forecasting with a top-down alignment of independent-level forecasts," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1405-1414.

    Cited by:

    1. Marco Zanotti, 2025. "Do global forecasting models require frequent retraining?," Working Papers 551, University of Milano-Bicocca, Department of Economics.
    2. Rombouts, Jeroen & Ternes, Marie & Wilms, Ines, 2025. "Cross-temporal forecast reconciliation at digital platforms with machine learning," International Journal of Forecasting, Elsevier, vol. 41(1), pages 321-344.
    3. Jeroen Rombouts & Marie Ternes & Ines Wilms, 2024. "Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning," Papers 2402.09033, arXiv.org, revised May 2024.
    4. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Panagiotelis, Anastasios, 2024. "Forecast reconciliation: A review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 430-456.
    5. de Azevedo Takara, Lucas & Teixeira, Ana Clara & Yazdanpanah, Hamed & Mariani, Viviana Cocco & dos Santos Coelho, Leandro, 2024. "Optimizing multi-step wind power forecasting: Integrating advanced deep neural networks with stacking-based probabilistic learning," Applied Energy, Elsevier, vol. 369(C).
    6. Redouane Benabdallah Benarmas & Kadda Beghdad Bey, 2024. "A deep learning hierarchical approach to road traffic forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1294-1307, August.
    7. Antoniadis, Anestis & Gaucher, Solenne & Goude, Yannig, 2024. "Hierarchical transfer learning with applications to electricity load forecasting," International Journal of Forecasting, Elsevier, vol. 40(2), pages 641-660.

  10. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 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 & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    See citations under working paper version above.
  11. Rui Pan & Tunan Ren & Baishan Guo & Feng Li & Guodong Li & Hansheng Wang, 2022. "A Note on Distributed Quantile Regression by Pilot Sampling and One-Step Updating," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1691-1700, October.

    Cited by:

    1. Wang, Kangning & Zhang, Jingyu & Sun, Xiaofei, 2025. "Adaptive distributed smooth composite quantile regression estimation for large-scale data," Computational Statistics & Data Analysis, Elsevier, vol. 204(C).
    2. Fan, Ye & Lin, Nan, 2025. "Sequential quantile regression for stream data by least squares," Journal of Econometrics, Elsevier, vol. 249(PA).
    3. Jun Jin & Qinghan Liang, 2026. "Communication-efficient distributed composite quantile regression via convolution smoothing and poisson subsampling," Statistical Papers, Springer, vol. 67(1), pages 1-55, February.
    4. Jun Jin & Chenyan Hao & Yewen Chen, 2025. "Composite quantile regression for a distributed system with non-randomly distributed data," Statistical Papers, Springer, vol. 66(1), pages 1-30, January.

  12. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.

    Cited by:

    1. Chatterjee, Sheshadri & Chaudhuri, Ranjan & Gupta, Shivam & Sivarajah, Uthayasankar & Bag, Surajit, 2023. "Assessing the impact of big data analytics on decision-making processes, forecasting, and performance of a firm," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    2. Dong, Yilun & Hao, Youzhi & Lu, Detang, 2025. "A framework for timely and accessible long-term forecasting of shale gas production based on time series pattern matching," International Journal of Forecasting, Elsevier, vol. 41(2), pages 821-843.

  13. Feng Li & Zhuojing He, 2019. "Credit risk clustering in a business group: Which matters more, systematic or idiosyncratic risk?," Cogent Economics & Finance, Taylor & Francis Journals, vol. 7(1), pages 1632528-163, January.

    Cited by:

    1. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 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 & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Chen, Xihui & Gu, Zhouyi & Esposito, Luca & Lv, Jiayan, 2025. "Overview of rural credit environment in China: Measurement logic, evaluation system, and case analysis," Evaluation and Program Planning, Elsevier, vol. 108(C).
    3. Gu, Zhouyi & Chen, Xihui & Parziale, Anna & Tang, Zhuoyuan, 2024. "Evaluation of primary-level credit environment, indicator system and empirical analysis: A case study of credit construction in China county and district," Evaluation and Program Planning, Elsevier, vol. 104(C).

  14. Li, Feng & Kang, Yanfei, 2018. "Improving forecasting performance using covariate-dependent copula models," International Journal of Forecasting, Elsevier, vol. 34(3), pages 456-476.

    Cited by:

    1. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 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 & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Xinpeng Geng & Bing Han & Debao Yang & Junren Zhao, 2024. "Credit risk contagion of supply chain finance: An empirical analysis of supply chain listed companies," PLOS ONE, Public Library of Science, vol. 19(8), pages 1-22, August.
    3. Rewat Khanthaporn, 2022. "Analysis of Nonlinear Comovement of Benchmark Thai Government Bond Yields," PIER Discussion Papers 183, Puey Ungphakorn Institute for Economic Research.
    4. Kajal Lahiri & Liu Yang, 2023. "Predicting binary outcomes based on the pair-copula construction," Empirical Economics, Springer, vol. 64(6), pages 3089-3119, June.

  15. Feng Li & Mattias Villani, 2013. "Efficient Bayesian Multivariate Surface Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 706-723, December.

    Cited by:

    1. Takuma Yoshida, 2017. "Nonlinear surface regression with dimension reduction method," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(1), pages 29-50, January.
    2. Thiyanga S. Talagala & Feng Li & Yanfei Kang, 2019. "Feature-based Forecast-Model Performance Prediction," Monash Econometrics and Business Statistics Working Papers 21/19, Monash University, Department of Econometrics and Business Statistics.
    3. Larissa C. Alves & Ronaldo Dias & Helio S. Migon, 2024. "Variational Bayesian Lasso for spline regression," Computational Statistics, Springer, vol. 39(4), pages 2039-2064, June.
    4. Talagala, Thiyanga S. & Li, Feng & Kang, Yanfei, 2022. "FFORMPP: Feature-based forecast model performance prediction," International Journal of Forecasting, Elsevier, vol. 38(3), pages 920-943.

Chapters

    Sorry, no citations of chapters recorded.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.