IDEAS home Printed from https://ideas.repec.org/r/pal/jorsoc/v52y2001i6d10.1057_palgrave.jors.2601133.html
   My bibliography  Save this item

Time series forecasting with neural network ensembles: an application for exchange rate prediction

Citations

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


Cited by:

  1. Vouldis, Angelos T. & Michaelides, Panayotis G. & Tsionas, Efthymios G., 2010. "Estimating semi-parametric output distance functions with neural-based reduced form equations using LIML," Economic Modelling, Elsevier, vol. 27(3), pages 697-704, May.
  2. Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. "“An application of deep learning for exchange rate forecasting”," AQR Working Papers 202201, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2022.
  3. Saira Al-Zadjali & Ahmed Al Maashri & Amer Al-Hinai & Sultan Al-Yahyai & Mostafa Bakhtvar, 2019. "An Accurate, Light-Weight Wind Speed Predictor for Renewable Energy Management Systems," Energies, MDPI, vol. 12(22), pages 1-20, November.
  4. R Setiono & S-L Pan & M-H Hsieh & A Azcarraga, 2006. "Knowledge acquisition and revision using neural networks: an application to a cross-national study of brand image perception," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(3), pages 231-240, March.
  5. Michaelides, Panayotis G. & Vouldis, Angelos T. & Tsionas, Efthymios G., 2010. "Globally flexible functional forms: The neural distance function," European Journal of Operational Research, Elsevier, vol. 206(2), pages 456-469, October.
  6. Michaelides, Panayotis G. & Tsionas, Efthymios G. & Vouldis, Angelos T. & Konstantakis, Konstantinos N., 2015. "Global approximation to arbitrary cost functions: A Bayesian approach with application to US banking," European Journal of Operational Research, Elsevier, vol. 241(1), pages 148-160.
  7. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
  8. Ortiz-Arango, Francisco & Cabrera-Llanos, Agustín I. & Venegas-Martínez, Francisco, 2014. "Euro Exchange Rate Forecasting with Differential Neural Networks with an Extended Tracking Procedure," MPRA Paper 57720, University Library of Munich, Germany.
  9. de Souza Vasconcelos, Camila & Hadad Júnior, Eli, 2023. "Forecasting exchange rate: A bibliometric and content analysis," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 607-628.
  10. Lukas Ryll & Sebastian Seidens, 2019. "Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey," Papers 1906.07786, arXiv.org, revised Jul 2019.
  11. Indranil Ghosh & Tamal Datta Chaudhuri, 2017. "Fractal Investigation and Maximal Overlap Discrete Wavelet Transformation (MODWT)-based Machine Learning Framework for Forecasting Exchange Rates," Studies in Microeconomics, , vol. 5(2), pages 105-131, December.
  12. Tamal Datta Chaudhuri & Indranil Ghosh, 2016. "Artificial Neural Network and Time Series Modeling Based Approach to Forecasting the Exchange Rate in a Multivariate Framework," Papers 1607.02093, arXiv.org.
  13. Castro-Iragorri, C & Ramírez, J, 2021. "Forecasting Dynamic Term Structure Models with Autoencoders," Documentos de Trabajo 19431, Universidad del Rosario.
  14. Sun, Shaolong & Wang, Shouyang & Wei, Yunjie, 2019. "A new multiscale decomposition ensemble approach for forecasting exchange rates," Economic Modelling, Elsevier, vol. 81(C), pages 49-58.
  15. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
  16. Olcay Erdogan & Ali Goksu, 2014. "Forecasting Euro and Turkish Lira Exchange Rates with Artificial Neural Networks (ANN)," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 4(4), pages 307-316, October.
  17. Yi-Hsien Wang, 2009. "Using neural network to forecast stock index option price: a new hybrid GARCH approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 43(5), pages 833-843, September.
  18. Panayotis G. Michaelides & Efthymios G. Tsionas & Angelos T. Vouldis & Konstantinos N. Konstantakis & Panagiotis Patrinos, 2018. "A Semi-Parametric Non-linear Neural Network Filter: Theory and Empirical Evidence," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 637-675, March.
  19. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
  20. Michaelides, Panayotis G. & Tsionas, Efthymios & Konstantakis, Konstantinos, 2016. "Financial Bubble Detection : A Non-Linear Method with Application to S&P 500," MPRA Paper 74477, University Library of Munich, Germany.
  21. Firat Melih Yilmaz & Ozer Arabaci, 2021. "Should Deep Learning Models be in High Demand, or Should They Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 217-245, January.
  22. Luis Manuel León Anaya & Víctor Manuel Landassuri Moreno & Héctor Rafael Orozco Aguirre & Maricela Quintana López, 2018. "Predicción del IPC mexicano combinando modelos econométricos e inteligencia artificial," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 13(4), pages 603-629, Octubre-D.
  23. Elsy Gómez-Ramos & Francisco Venegas-Martínez, 2013. "A Review of Artificial Neural Networks: How Well Do They Perform in Forecasting Time Series?," Analítika, Analítika - Revista de Análisis Estadístico/Journal of Statistical Analysis, vol. 6(2), pages 7-15, Diciembre.
  24. Raul Enrique Rodriguez Luna & Jose Luis Rosenstiehl Martinez, 2022. "Digital Transformation of Nature Tourism," Papers 2211.03945, arXiv.org.
  25. Michaelides, Panayotis G. & Tsionas, Efthymios G. & Konstantakis, Konstantinos N., 2016. "Non-linearities in financial bubbles: Theory and Bayesian evidence from S&P500," Journal of Financial Stability, Elsevier, vol. 24(C), pages 61-70.
  26. Tsionas, Mike G. & Andrikopoulos, Athanasios, 2020. "On a High-Dimensional Model Representation method based on Copulas," European Journal of Operational Research, Elsevier, vol. 284(3), pages 967-979.
  27. Barrow, Devon K. & Crone, Sven F., 2016. "Cross-validation aggregation for combining autoregressive neural network forecasts," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1120-1137.
  28. Ortíz Arango Francisco & Cabrera Llanos Agustín Ignacio & López Herrera Francisco, 2013. "Pronóstico de los índices accionarios DAX y S&P 500 con redes neuronales diferenciales," Contaduría y Administración, Accounting and Management, vol. 58(3), pages 203-225, julio-sep.
  29. Cornelius Köpp & Hans-Jörg Mettenheim & Michael Breitner, 2014. "Decision Analytics with Heatmap Visualization for Multi-step Ensemble Data," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(3), pages 131-140, June.
  30. Rodriguez Luna, Raúl Enrique & Rosenstiehl Martinez, José Luis, 2022. "Endogenización como mecanismo evolutivo para la transformación digital de las pymes de turismo de naturaleza," Revista Tendencias, Universidad de Narino, vol. 23(1), pages 117-138, January.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.