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To combine or not to combine: selecting among forecasts and their combinations

Citations

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Cited by:

  1. von der Gracht, Heiko A. & Hommel, Ulrich & Prokesch, Tobias & Wohlenberg, Holger, 2016. "Testing weighting approaches for forecasting in a Group Wisdom Support System environment," Journal of Business Research, Elsevier, vol. 69(10), pages 4081-4094.
  2. Gibson, Heather D. & Hall, Stephen G. & Tavlas, George S., 2020. "Nonlinear forecast combinations: An example using euro-area real GDP growth," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 579-589.
  3. Zhang, Gang & Yang, Dazhi & Galanis, George & Androulakis, Emmanouil, 2022. "Solar forecasting with hourly updated numerical weather prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
  4. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
  5. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
  6. João F. Caldeira & Guilherme V. Moura & Francisco J. Nogales & André A. P. Santos, 2017. "Combining Multivariate Volatility Forecasts: An Economic-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 15(2), pages 247-285.
  7. Fotios Petropoulos & Enno Siemsen, 2023. "Forecast Selection and Representativeness," Management Science, INFORMS, vol. 69(5), pages 2672-2690, May.
  8. Bacci, Livio Agnew & Mello, Luiz Gustavo & Incerti, Taynara & Paulo de Paiva, Anderson & Balestrassi, Pedro Paulo, 2019. "Optimization of combined time series methods to forecast the demand for coffee in Brazil: A new approach using Normal Boundary Intersection coupled with mixture designs of experiments and rotated factor scores," International Journal of Production Economics, Elsevier, vol. 212(C), pages 186-211.
  9. Schwarzmüller, Tim, 2015. "Model pooling and changes in the informational content of predictors: An empirical investigation for the euro area," Kiel Working Papers 1982, Kiel Institute for the World Economy.
  10. Uniejewski, Bartosz & Maciejowska, Katarzyna, 2023. "LASSO principal component averaging: A fully automated approach for point forecast pooling," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1839-1852.
  11. Drechsel, Katja & Scheufele, Rolf, 2012. "The performance of short-term forecasts of the German economy before and during the 2008/2009 recession," International Journal of Forecasting, Elsevier, vol. 28(2), pages 428-445.
  12. Qian, Fanyue & Gao, Weijun & Yang, Yongwen & Yu, Dan, 2020. "Potential analysis of the transfer learning model in short and medium-term forecasting of building HVAC energy consumption," Energy, Elsevier, vol. 193(C).
  13. Zvi Schwartz & Timothy Webb & Jean-Pierre I van der Rest & Larissa Koupriouchina, 2021. "Enhancing the accuracy of revenue management system forecasts: The impact of machine and human learning on the effectiveness of hotel occupancy forecast combinations across multiple forecasting horizons," Tourism Economics, , vol. 27(2), pages 273-291, March.
  14. Korprasertsak, Natapol & Leephakpreeda, Thananchai, 2019. "Robust short-term prediction of wind power generation under uncertainty via statistical interpretation of multiple forecasting models," Energy, Elsevier, vol. 180(C), pages 387-397.
  15. Jiang, Jingjing & Ye, Bin & Liu, Junguo, 2019. "Research on the peak of CO2 emissions in the developing world: Current progress and future prospect," Applied Energy, Elsevier, vol. 235(C), pages 186-203.
  16. Chiu, Yen-Chen & Chuang, I-Yuan & Lai, Jing-Yi, 2010. "The performance of composite forecast models of value-at-risk in the energy market," Energy Economics, Elsevier, vol. 32(2), pages 423-431, March.
  17. Laurence Fung & Ip-wing Yu, 2008. "Predicting Stock Market Returns by Combining Forecasts," Working Papers 0801, Hong Kong Monetary Authority.
  18. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
  19. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
  20. Jakub Nowotarski & Rafal Weron, 2016. "To combine or not to combine? Recent trends in electricity price forecasting," HSC Research Reports HSC/16/01, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
  21. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices," Energies, MDPI, vol. 13(14), pages 1-19, July.
  22. Hong Chang & Wei Sun & Xingsheng Gu, 2013. "Forecasting Energy CO 2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model," Energies, MDPI, vol. 6(3), pages 1-22, March.
  23. Massimiliano Giacalone, 2022. "Optimal forecasting accuracy using Lp-norm combination," METRON, Springer;Sapienza Università di Roma, vol. 80(2), pages 187-230, August.
  24. Karsten Hueffer & Miguel A. Fonseca & Anthony Leiserowitz & Karen M. Taylor, 2013. "The wisdom of crowds: Predicting a weather and climate-related event," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 8(2), pages 91-105, March.
  25. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
  26. Ma, Feng & Wahab, M.I.M. & Zhang, Yaojie, 2019. "Forecasting the U.S. stock volatility: An aligned jump index from G7 stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 54(C), pages 132-146.
  27. Zhang, Hanyuan & Liu, Ying & Liu, Xinyang & Liu, Anyu & Lin, Vera Shanshan, 2026. "Forecasting Chinese outbound tourism recovery: A Triple-layer forecast combination framework," Annals of Tourism Research, Elsevier, vol. 116(C).
  28. Rania A. H. Mohamed, 2023. "Enhancing forecast accuracy using combination methods for the hierarchical time series approach," PLOS ONE, Public Library of Science, vol. 18(7), pages 1-18, July.
  29. 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.
  30. Drechsel, Katja & Scheufele, Rolf, 2010. "Should We Trust in Leading Indicators? Evidence from the Recent Recession," IWH Discussion Papers 10/2010, Halle Institute for Economic Research (IWH).
  31. Beltrán, Sergio & Castro, Alain & Irizar, Ion & Naveran, Gorka & Yeregui, Imanol, 2022. "Framework for collaborative intelligence in forecasting day-ahead electricity price," Applied Energy, Elsevier, vol. 306(PA).
  32. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
  33. Alexander Vosseler & Enzo Weber, 2018. "Forecasting seasonal time series data: a Bayesian model averaging approach," Computational Statistics, Springer, vol. 33(4), pages 1733-1765, December.
  34. Adebayo Kutu & Gbenga Akinola & Ntokozo Nzimande, 2016. "Monetary Policy and Output Growth Forecasting in a SVAR Perspective," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(7), pages 1-71, July.
  35. Graefe, Andreas & Armstrong, J. Scott & Jones, Randall J. & Cuzán, Alfred G., 2014. "Combining forecasts: An application to elections," International Journal of Forecasting, Elsevier, vol. 30(1), pages 43-54.
  36. Monge, Manuel & Claudio-Quiroga, Gloria & Poza, Carlos, 2024. "Chinese economic behavior in times of covid-19. A new leading economic indicator based on Google trends," International Economics, Elsevier, vol. 177(C).
  37. Jin, Ming & Feng, Wei & Liu, Ping & Marnay, Chris & Spanos, Costas, 2017. "MOD-DR: Microgrid optimal dispatch with demand response," Applied Energy, Elsevier, vol. 187(C), pages 758-776.
  38. Halkos, George & Tzirivis, Apostolos, 2018. "Effective energy commodities’ risk management: Econometric modeling of price volatility," MPRA Paper 90781, University Library of Munich, Germany.
  39. Emrah Gulay & Serkan Aras, 2024. "Does a meta-combining method lead to more accurate forecasts in the decision-making process?," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(3), pages 101-124.
  40. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
  41. López Menéndez, Ana Jesús & Pérez Suárez, Rigoberto, 2017. "Forecasting Performance and Information Measures. Revisiting the M-Competition /Evaluación de Predicciones y Medidas de Información. Reexamen de la M-Competición," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 35, pages 299-314, Mayo.
  42. Peter Kacmary & Andrea Rosova & Marian Sofranko & Peter Bindzar & Janka Saderova & Jan Kovac, 2021. "Creation of Annual Order Forecast for the Production of Beverage Cans—The Case Study," Sustainability, MDPI, vol. 13(15), pages 1-14, July.
  43. Ma, Feng & Li, Yu & Liu, Li & Zhang, Yaojie, 2018. "Are low-frequency data really uninformative? A forecasting combination perspective," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 92-108.
  44. Roth, Alvin E. & Herzog, Stefan & Hau, Robin & Hertwig, Ralph & Erev, Ido & Ert, Eyal & Haruvy, Ernan & Stewart, Terrence & West, Robert & Lebiere, Christian, 2009. "A Choice Prediction Competition: Choices From Experience and From Description," Scholarly Articles 5343169, Harvard University Department of Economics.
  45. Kostas Andriosopoulos & Nikos Nomikos, 2012. "Risk management in the energy markets and Value-at-Risk modelling: a Hybrid approach," RSCAS Working Papers 2012/47, European University Institute.
  46. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
  47. Szymon Lis & Marcin Chlebus, 2021. "Comparison of the accuracy in VaR forecasting for commodities using different methods of combining forecasts," Working Papers 2021-11, Faculty of Economic Sciences, University of Warsaw.
  48. Mingming Hu & Haifeng Yang & Doris Chenguang Wu & Shuai Ma, 2024. "A novel two-stage combination model for tourism demand forecasting," Tourism Economics, , vol. 30(8), pages 1925-1950, December.
  49. Lu, Jin-Ray & Lee, Pei-Hsuan & Chuang, I-Yuan, 2011. "Estimation of oil firm's systematic risk via composite time-varying models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(11), pages 2389-2399.
  50. Chan, Chi Kin & Witt, Stephen F. & Lee, Y.C.E. & Song, H., 2010. "Tourism forecast combination using the CUSUM technique," Tourism Management, Elsevier, vol. 31(6), pages 891-897.
  51. Bordignon, Silvano & Bunn, Derek W. & Lisi, Francesco & Nan, Fany, 2013. "Combining day-ahead forecasts for British electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 88-103.
  52. Gustavo A. Marrero, 2007. "Traditional versus unobserved components methods to forecast quarterly national account aggregates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(2), pages 129-153.
  53. Dominik Stempie'n & Robert 'Slepaczuk, 2025. "Hybrid Models for Financial Forecasting: Combining Econometric, Machine Learning, and Deep Learning Models," Papers 2505.19617, arXiv.org.
  54. Chang, G.W. & Lu, H.J. & Chang, Y.R. & Lee, Y.D., 2017. "An improved neural network-based approach for short-term wind speed and power forecast," Renewable Energy, Elsevier, vol. 105(C), pages 301-311.
  55. Yang, Dazhi & Yang, Guoming & Liu, Bai, 2023. "Combining quantiles of calibrated solar forecasts from ensemble numerical weather prediction," Renewable Energy, Elsevier, vol. 215(C).
  56. 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.
  57. Gandoman, Foad H. & Raeisi, Fatima & Ahmadi, Abdollah, 2016. "A literature review on estimating of PV-array hourly power under cloudy weather conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 579-592.
  58. Pedro M.D.C.B. Gouveia & Denise R. Osborn & Paulo M.M. Rodrigues, 2008. "Comparing Seasonal Forecasts of Industrial Production," Centre for Growth and Business Cycle Research Discussion Paper Series 102, Economics, The University of Manchester.
  59. Wei Sun & Jingmin Wang & Hong Chang, 2012. "Forecasting Annual Power Generation Using a Harmony Search Algorithm-Based Joint Parameters Optimization Combination Model," Energies, MDPI, vol. 5(10), pages 1-24, October.
  60. Huiyu Huang & Tae-Hwy Lee, 2010. "To Combine Forecasts or to Combine Information?," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 534-570.
  61. Thomas B Götz & Klemens Hauzenberger, 2021. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 442-461.
  62. Mishra, G. C. & Singh, A., . "A Study on Forecasting Prices of Groundnut Oil in Delhi by Arima Methodology and Artificial Neural Networks," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 5(3), pages 1-10.
  63. Shaub, David, 2020. "Fast and accurate yearly time series forecasting with forecast combinations," International Journal of Forecasting, Elsevier, vol. 36(1), pages 116-120.
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