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Artificial neural network models for forecasting and decision making

Citations

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Cited by:

  1. Birol Yildiz & Ari Yezegel, 2010. "Fundamental Analysis With Artificial Neural Network," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 4(1), pages 149-158.
  2. Khondker Mohammad Zobair & Louis Sanzogni & Luke Houghton & Md Zahidul Islam, 2021. "Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-31, September.
  3. Prybutok, Victor R. & Yi, Junsub & Mitchell, David, 2000. "Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations," European Journal of Operational Research, Elsevier, vol. 122(1), pages 31-40, April.
  4. Mioara CHIRITA & Daniela SARPE, 2011. "Usefulness of Artificial Neural Networks for Predicting Financial and Economic Crisis," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 44-48.
  5. Daniel Santin, 2008. "On the approximation of production functions: a comparison of artificial neural networks frontiers and efficiency techniques," Applied Economics Letters, Taylor & Francis Journals, vol. 15(8), pages 597-600.
  6. C, Deep Prakash & Majumdar, Adrija, 2023. "Predicting sports fans’ engagement with culturally aligned social media content: A language expectancy perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
  7. Anshuka Anshuka & Floris F. van Ogtrop & R. Willem Vervoort, 2019. "Drought forecasting through statistical models using standardised precipitation index: a systematic review and meta-regression analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(2), pages 955-977, June.
  8. Amiri, Arshia & Bakhshoodeh, Mohamad & Najafi, Bahaeddin, 2011. "Forecasting seasonality in prices of potatoes and onions: challenge between geostatistical models, neuro fuzzy approach and Winter method," MPRA Paper 34093, University Library of Munich, Germany.
  9. Leung, Philip C.M. & Lee, Eric W.M., 2013. "Estimation of electrical power consumption in subway station design by intelligent approach," Applied Energy, Elsevier, vol. 101(C), pages 634-643.
  10. Caputo, Antonio C. & Pelagagge, Pacifico M., 2008. "Parametric and neural methods for cost estimation of process vessels," International Journal of Production Economics, Elsevier, vol. 112(2), pages 934-954, April.
  11. Jin, Huaiping & Shi, Lixian & Chen, Xiangguang & Qian, Bin & Yang, Biao & Jin, Huaikang, 2021. "Probabilistic wind power forecasting using selective ensemble of finite mixture Gaussian process regression models," Renewable Energy, Elsevier, vol. 174(C), pages 1-18.
  12. Cadenas, Erasmo & Rivera, Wilfrido, 2009. "Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks," Renewable Energy, Elsevier, vol. 34(1), pages 274-278.
  13. Sümeyye Çelik, 2020. "Determination and Classification of Importance of Attributes Used in Diagnosing Pregnant Women's Birth Method," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(2), pages 261-274, December.
  14. Chatfield, Chris, 1995. "Positive or negative?," International Journal of Forecasting, Elsevier, vol. 11(4), pages 501-502, December.
  15. Oscar Claveria & Salvador Torra, 2013. "“Forecasting Business surveys indicators: neural networks vs. time series models”," AQR Working Papers 201312, University of Barcelona, Regional Quantitative Analysis Group, revised Nov 2013.
  16. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents' expectations. Different patterns of anticipation of the 2008 financial crisis”," IREA Working Papers 201511, University of Barcelona, Research Institute of Applied Economics, revised Mar 2015.
  17. Eleimon Gonis & Salima Paul & Jon Tucker, 2012. "Rating or no rating? That is the question: an empirical examination of UK companies," The European Journal of Finance, Taylor & Francis Journals, vol. 18(8), pages 709-735, September.
  18. Gutierrez, Rafael S. & Solis, Adriano O. & Mukhopadhyay, Somnath, 2008. "Lumpy demand forecasting using neural networks," International Journal of Production Economics, Elsevier, vol. 111(2), pages 409-420, February.
  19. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
  20. 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.
  21. Beat Reber & Bob Berry & Steve Toms, 2005. "Predicting mispricing of initial public offerings," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 13(1), pages 41-59, March.
  22. Thomassey, Sebastien & Happiette, Michel & Castelain, Jean Marie, 2005. "A short and mean-term automatic forecasting system--application to textile logistics," European Journal of Operational Research, Elsevier, vol. 161(1), pages 275-284, February.
  23. Dimitrios K. Panagiotou & Anastasios I. Dounis, 2022. "Comparison of Hospital Building’s Energy Consumption Prediction Using Artificial Neural Networks, ANFIS, and LSTM Network," Energies, MDPI, vol. 15(17), pages 1-25, September.
  24. Mioara CHIRITA, 2012. "Usefulness of Artificial Neural Networks for Predicting Financial and Economic Crisis," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 61-66.
  25. Arshia Amiri & Ulf-G Gerdtham, 2012. "Granger Causality Between Exports, Imports and GDP in France: Evidance from Using Geostatistical Models," Economic Research Guardian, Weissberg Publishing, vol. 2(1), pages 43-59, May.
  26. Wu, Yih-Jiuan, 1998. "Exchange rate forecasting: an application of radial basis function neural networks," ISU General Staff Papers 1998010108000013540, Iowa State University, Department of Economics.
  27. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
  28. Mahla Nikou & Gholamreza Mansourfar & Jamshid Bagherzadeh, 2019. "Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(4), pages 164-174, October.
  29. Pei En Lee, 2019. "The Empirical Study of Investor Sentiment on Stock Return Prediction," International Journal of Economics and Financial Issues, Econjournals, vol. 9(2), pages 119-124.
  30. Welch, Eric & Bretschneider, Stuart & Rohrbaugh, John, 1998. "Accuracy of judgmental extrapolation of time series data: Characteristics, causes, and remediation strategies for forecasting," International Journal of Forecasting, Elsevier, vol. 14(1), pages 95-110, March.
  31. Lolli, F. & Gamberini, R. & Regattieri, A. & Balugani, E. & Gatos, T. & Gucci, S., 2017. "Single-hidden layer neural networks for forecasting intermittent demand," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 116-128.
  32. Tabandeh, Razieh & jusoh, mansor & Md Noor, Nor Ghani & Zaidi, Mohd Azlan Shah, 2013. "Causes of Tax Evasion and Their Relative Contribution in Malaysia: An Artificial Neural Network Method Analysis," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 47(1), pages 99-108.
  33. Rä‚Zvan Popa, 2020. "Improving Earnings Predictions With Neural Network Models," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 26, pages 77-96, December.
  34. Jorge Perez-Rodriguez & Salvador Torra & Julian Andrada-Felix, 2005. "Are Spanish Ibex35 stock future index returns forecasted with non-linear models?," Applied Financial Economics, Taylor & Francis Journals, vol. 15(14), pages 963-975.
  35. Amiri, Arshia & Zibaei, Mansour, 2012. "Granger causality between energy use and economic growth in France with using geostatistical models," MPRA Paper 36357, University Library of Munich, Germany.
  36. Geraint Johnes, 2000. "Up Around the Bend: Linear and nonlinear models of the UK economy compared," International Review of Applied Economics, Taylor & Francis Journals, vol. 14(4), pages 485-493.
  37. C. Orsenigo & C. Vercellis, 2018. "Anthropogenic influence on global warming for effective cost-benefit analysis: a machine learning perspective," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 45(3), pages 425-442, September.
  38. Eakins, Stanley G. & Stansell, Stanley R., 2003. "Can value-based stock selection criteria yield superior risk-adjusted returns: an application of neural networks," International Review of Financial Analysis, Elsevier, vol. 12(1), pages 83-97.
  39. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  40. Yi Junsub & Prybutok Victor R. & Clayton Howard R., 2001. "ARL Comparisons Between Neural Network Models and -Control Charts for Quality Characteristics that are Nonnormally Distributed," Stochastics and Quality Control, De Gruyter, vol. 16(1), pages 5-15, January.
  41. D. J. E. Baestaens, 1997. "Comment," The European Journal of Finance, Taylor & Francis Journals, vol. 3(3), pages 203-224.
  42. Gencay, Ramazan & Selcuk, Faruk, 2001. "Software reviews," International Journal of Forecasting, Elsevier, vol. 17(2), pages 305-317.
  43. Icen Yoosefdoost & Abbas Khashei-Siuki & Hossein Tabari & Omolbani Mohammadrezapour, 2022. "Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1191-1215, March.
  44. Callen, Jeffrey L. & Kwan, Clarence C. Y. & Yip, Patrick C. Y. & Yuan, Yufei, 1996. "Neural network forecasting of quarterly accounting earnings," International Journal of Forecasting, Elsevier, vol. 12(4), pages 475-482, December.
  45. Zhang, Nan & Hwang, Bon-Gang & Lu, Yujie & Ngo, Jasmine, 2022. "A Behavior theory integrated ANN analytical approach for understanding households adoption decisions of residential photovoltaic (RPV) system," Technology in Society, Elsevier, vol. 70(C).
  46. Paulo M.M. Rodrigues & Nazarii Salish, 2011. "Modeling and Forecasting Interval Time Series with Threshold Models: An Application to S&P500 Index Returns," Working Papers w201128, Banco de Portugal, Economics and Research Department.
  47. Wahid Ali Hamood Altowayti & Shafinaz Shahir & Taiseer Abdalla Elfadil Eisa & Maged Nasser & Muhammad Imran Babar & Abdullah Faisal Alshalif & Faris Ali Hamood AL-Towayti, 2022. "Smart Modelling of a Sustainable Biological Wastewater Treatment Technologies: A Critical Review," Sustainability, MDPI, vol. 14(22), pages 1-32, November.
  48. Thomassey, Sebastien & Happiette, Michel & Castelain, Jean-Marie, 2005. "A global forecasting support system adapted to textile distribution," International Journal of Production Economics, Elsevier, vol. 96(1), pages 81-95, April.
  49. Stolzke, U.A. & Müller, R.A.E., 1997. "Neuere Verfahren der Preisprognose aus Sicht des Agrarhandels," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 33.
  50. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
  51. de Lucio, Juan, 2021. "Estimación adelantada del crecimiento regional mediante redes neuronales LSTM," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 49, pages 45-64.
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