IDEAS home Printed from https://ideas.repec.org/r/eee/intfor/v34y2018i1p64-74.html
   My bibliography  Save this item

Some theoretical results on forecast combinations

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
  2. Zhen Chu & Mingwang Cheng & Ning Neil Yu, 2022. "Development potential of Chinese smart cities and its spatio‐temporal pattern: A new hybrid MADM method using combination weight," Growth and Change, Wiley Blackwell, vol. 53(4), pages 1546-1566, December.
  3. Pauwels, Laurent & Radchenko, Peter & Vasnev, Andrey, 2019. "Higher Moment Constraints for Predictive Density Combinations," Working Papers BAWP-2019-01, University of Sydney Business School, Discipline of Business Analytics.
  4. Pinto, Jeronymo Marcondes & Marçal, Emerson Fernandes, 2019. "Cross-validation based forecasting method: a machine learning approach," Textos para discussão 498, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
  5. 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.
  6. Chuanhua Wei & Chenping Du & Nana Zheng, 2020. "A Changing Weights Spatial Forecast Combination Approach with an Application to Housing Price Prediction," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 12(4), pages 1-11, April.
  7. 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.
  8. Grzegorz Dudek, 2022. "A Comprehensive Study of Random Forest for Short-Term Load Forecasting," Energies, MDPI, vol. 15(20), pages 1-19, October.
  9. Chan, Felix & Pauwels, Laurent, 2019. "Equivalence of optimal forecast combinations under affine constraints," Working Papers BAWP-2019-02, University of Sydney Business School, Discipline of Business Analytics.
  10. Aysun Kapucugil Ikiz & Gizem Halil Utma, 2023. "Combined Forecasts of Intermittent Demand for Stock-keeping Units (SKUs)," World Journal of Applied Economics, WERI-World Economic Research Institute, vol. 9(1), pages 1-31, June.
  11. 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.
  12. Zongwu Cai & Chaoqun Ma & Xianhua Mi, 2020. "Realized Volatility Forecasting Based on Dynamic Quantile Model Averaging," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202016, University of Kansas, Department of Economics, revised Sep 2020.
  13. Saidjon Shiralievich Tavarov & Alexander Sidorov & Zsolt Čonka & Murodbek Safaraliev & Pavel Matrenin & Mihail Senyuk & Svetlana Beryozkina & Inga Zicmane, 2023. "Control of Operational Modes of an Urban Distribution Grid under Conditions of Uncertainty," Energies, MDPI, vol. 16(8), pages 1-18, April.
  14. Li, Haohua & Mei, Yuhe & Hao, Xianfeng & Chen, Zhuo, 2024. "Out-of-sample equity premium predictability: An EMD-denoising based model," Pacific-Basin Finance Journal, Elsevier, vol. 88(C).
  15. Post, Thierry & Karabatı, Selçuk & Arvanitis, Stelios, 2019. "Robust optimization of forecast combinations," International Journal of Forecasting, Elsevier, vol. 35(3), pages 910-926.
  16. Xin Gao & Xiaobing Li & Bing Zhao & Weijia Ji & Xiao Jing & Yang He, 2019. "Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection," Energies, MDPI, vol. 12(6), pages 1-18, March.
  17. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
  18. Zhentao Shi & Liangjun Su & Tian Xie, 2020. "L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis," Papers 2010.09477, arXiv.org, revised Aug 2022.
  19. Wang, Yudong & Hao, Xianfeng & Wu, Chongfeng, 2021. "Forecasting stock returns: A time-dependent weighted least squares approach," Journal of Financial Markets, Elsevier, vol. 53(C).
  20. Kourentzes, Nikolaos & Barrow, Devon & Petropoulos, Fotios, 2019. "Another look at forecast selection and combination: Evidence from forecast pooling," International Journal of Production Economics, Elsevier, vol. 209(C), pages 226-235.
  21. Jiawen Luo & Shengjie Fu & Oguzhan Cepni & Rangan Gupta, 2024. "Climate Risks and Forecastability of US Inflation: Evidence from Dynamic Quantile Model Averaging," Working Papers 202420, University of Pretoria, Department of Economics.
  22. Eraslan, Sercan & Nöller, Marvin, 2020. "Recession probabilities falling from the STARs," Discussion Papers 08/2020, Deutsche Bundesbank.
  23. Ji Wu & Xian Cheng & Stephen Shaoyi Liao, 2020. "Tourism forecast combination using the stochastic frontier analysis technique," Tourism Economics, , vol. 26(7), pages 1086-1107, November.
  24. 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.
  25. Li Liu & Xianfeng Hao & Yudong Wang, 2024. "Solving the Forecast Combination Puzzle Using Double Shrinkages," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(3), pages 714-741, June.
  26. Jesús Molina‐Muñoz & Andrés Mora‐Valencia & Javier Perote, 2024. "Predicting carbon and oil price returns using hybrid models based on machine and deep learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
  27. Zhikai Zhang & Yaojie Zhang & Yudong Wang, 2024. "Forecasting the equity premium using weighted regressions: Does the jump variation help?," Empirical Economics, Springer, vol. 66(5), pages 2049-2082, May.
  28. Umair Muneer Butt & Sukumar Letchmunan & Fadratul Hafinaz Hassan & Tieng Wei Koh, 2022. "Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy," PLOS ONE, Public Library of Science, vol. 17(9), pages 1-22, September.
  29. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.