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Linear versus neural network forecasts for European industrial production series

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Cited by:

  1. Fullerton, Thomas M., Jr. & Mukhopadhyay, Somnath, 2013. "Border Region Bridge and Air Transport Predictability," MPRA Paper 59583, University Library of Munich, Germany, revised 11 Jul 2013.
  2. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
  3. Kristóf Németh & Dániel Hadházi, 2026. "GDP Nowcasting With Artificial Neural Networks: How Much Does Long‐Term Memory Matter?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(3), pages 924-963, April.
  4. Pakravan, Mohammad Reza & Kalashami, Mohammad Kavoosi, 2011. "Future prospects of Iran, U.S and Turkey's Pistachio exports," International Journal of Agricultural Management and Development (IJAMAD), Iranian Association of Agricultural Economics, vol. 1(3), pages 1-8, September.
  5. Yongmei Fang & Emmanuel Sirimal Silva & Bo Guan & Hossein Hassani & Saeed Heravi, 2025. "Optimal Forecast Combination for Japanese Tourism Demand," Tourism and Hospitality, MDPI, vol. 6(2), pages 1-19, May.
  6. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
  7. Galdi, Giulio & Casarin, Roberto & Ferrari, Davide & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Nowcasting industrial production using linear and non-linear models of electricity demand," Energy Economics, Elsevier, vol. 126(C).
  8. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
  9. 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.
  10. Donya Rahmani & Saeed Heravi & Hossein Hassani & Mansi Ghodsi, 2016. "Forecasting time series with structural breaks with Singular Spectrum Analysis, using a general form of recurrent formula," Papers 1605.02188, arXiv.org.
  11. Hassani, Hossein & Heravi, Saeed & Zhigljavsky, Anatoly, 2009. "Forecasting European industrial production with singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 25(1), pages 103-118.
  12. Angelini, Eliana & di Tollo, Giacomo & Roli, Andrea, 2008. "A neural network approach for credit risk evaluation," The Quarterly Review of Economics and Finance, Elsevier, vol. 48(4), pages 733-755, November.
  13. Konstantinos Nikolopoulos, 2010. "Forecasting with quantitative methods: the impact of special events in time series," Applied Economics, Taylor & Francis Journals, vol. 42(8), pages 947-955.
  14. Mahdi Kalantari & Hossein Hassani, 2019. "Automatic Grouping in Singular Spectrum Analysis," Forecasting, MDPI, vol. 1(1), pages 1-16, October.
  15. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
  16. Krist'of N'emeth & D'aniel Hadh'azi, 2023. "GDP nowcasting with artificial neural networks: How much does long-term memory matter?," Papers 2304.05805, arXiv.org, revised Jan 2025.
  17. 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.
  18. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
  19. Loermann, Julius & Maas, Benedikt, 2019. "Nowcasting US GDP with artificial neural networks," MPRA Paper 95459, University Library of Munich, Germany.
  20. Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021. "Applications of machine learning for corporate bond yield spread forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
  21. Pakravan, Mohammad Reza & Kavoosi Kalashami, Mohammad & Alipour, Hamid Reza, 2011. "Forecasting Iran’s Rice Imports Trend During 2009-2013," International Journal of Agricultural Management and Development (IJAMAD), Iranian Association of Agricultural Economics, vol. 1(01), pages 1-6, March.
  22. Dipshikha Podder & Rohan Mukherjee & Gourishankar S. Hiremath, 2026. "Can Deep-Learning Models Predict Behavior of Treasury Bond Yields," Computational Economics, Springer;Society for Computational Economics, vol. 67(5), pages 4127-4148, May.
  23. Singh Devesh, 2021. "Interpretable Machine-Learning Approach in Estimating FDI Inflow: Visualization of ML Models with LIME and H2O," TalTech Journal of European Studies, Sciendo, vol. 11(1), pages 133-152, May.
  24. Charles S. Bos & Siem Jan Koopman, 2010. "Models with Time-varying Mean and Variance: A Robust Analysis of U.S. Industrial Production," Tinbergen Institute Discussion Papers 10-017/4, Tinbergen Institute.
  25. Donya Rahmani & Damien Fay, 2022. "A state‐dependent linear recurrent formula with application to time series with structural breaks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 43-63, January.
  26. Manogna R L & Aswini Kumar Mishra, 2021. "Forecasting spot prices of agricultural commodities in India: Application of deep‐learning models," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 72-83, January.
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