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Neural Network Models for Time Series Forecasts

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  1. Leigh, W. & Paz, M. & Purvis, R., 2002. "An analysis of a hybrid neural network and pattern recognition technique for predicting short-term increases in the NYSE composite index," Omega, Elsevier, vol. 30(2), pages 69-76, April.
  2. Yi-Ting Chen & Edward W. Sun & Yi-Bing Lin, 2020. "Machine learning with parallel neural networks for analyzing and forecasting electricity demand," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 569-597, August.
  3. Zoran Vojinovic & Vojislav Kecman & Rainer Seidel, 2001. "A data mining approach to financial time series modelling and forecasting," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(4), pages 225-239, December.
  4. Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad, 2011. "Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange," International Journal of Forecasting, Elsevier, vol. 27(3), pages 804-816, July.
  5. Yoshio Kajitani & A. Ian Mcleod & Keith W. Hipel, 2005. "Forecasting nonlinear time series with feed-forward neural networks: a case study of Canadian lynx data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 105-117.
  6. Ali Babikir & Henry Mwambi, 2016. "Evaluating the combined forecasts of the dynamic factor model and the artificial neural network model using linear and nonlinear combining methods," Empirical Economics, Springer, vol. 51(4), pages 1541-1556, December.
  7. Xiaodan Zhu & Anh Ninh & Hui Zhao & Zhenming Liu, 2021. "Demand Forecasting with Supply‐Chain Information and Machine Learning: Evidence in the Pharmaceutical Industry," Production and Operations Management, Production and Operations Management Society, vol. 30(9), pages 3231-3252, September.
  8. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
  9. Andreou Andreas S. & Zombanakis George A. & Migiakis Petros M., 2013. "On Defence Expenditure Reduction: Balancing Between Austerity and Security in Greece," Peace Economics, Peace Science, and Public Policy, De Gruyter, vol. 19(3), pages 437-458, December.
  10. Giuseppe Ciaburro & Gino Iannace, 2021. "Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review," Data, MDPI, vol. 6(6), pages 1-30, May.
  11. Hwarng, H. Brian, 2001. "Insights into neural-network forecasting of time series corresponding to ARMA(p,q) structures," Omega, Elsevier, vol. 29(3), pages 273-289, June.
  12. Fischer, Thomas & Krauss, Christopher & Treichel, Alex, 2018. "Machine learning for time series forecasting - a simulation study," FAU Discussion Papers in Economics 02/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
  13. Niu, Tong & Li, Jinkai & Wei, Wei & Yue, Hui, 2022. "A hybrid deep learning framework integrating feature selection and transfer learning for multi-step global horizontal irradiation forecasting," Applied Energy, Elsevier, vol. 326(C).
  14. Moisan, Stella & Herrera, Rodrigo & Clements, Adam, 2018. "A dynamic multiple equation approach for forecasting PM2.5 pollution in Santiago, Chile," International Journal of Forecasting, Elsevier, vol. 34(4), pages 566-581.
  15. Alekseev, K.P.G. & Seixas, J.M., 2009. "A multivariate neural forecasting modeling for air transport – Preprocessed by decomposition: A Brazilian application," Journal of Air Transport Management, Elsevier, vol. 15(5), pages 212-216.
  16. Ghiassi, M. & Saidane, H. & Zimbra, D.K., 2005. "A dynamic artificial neural network model for forecasting time series events," International Journal of Forecasting, Elsevier, vol. 21(2), pages 341-362.
  17. Syouching Lai & Hungchih Li, 2006. "The predictive power of quarterly earnings per share based on time series and artificial intelligence model," Applied Financial Economics, Taylor & Francis Journals, vol. 16(18), pages 1375-1388.
  18. Azadeh, A. & Saberi, M. & Seraj, O., 2010. "An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran," Energy, Elsevier, vol. 35(6), pages 2351-2366.
  19. 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.
  20. Duong Tran Anh & Thanh Duc Dang & Song Pham Van, 2019. "Improved Rainfall Prediction Using Combined Pre-Processing Methods and Feed-Forward Neural Networks," J, MDPI, vol. 2(1), pages 1-19, February.
  21. Hakob GRIGORYAN, 2016. "A Stock Market Prediction Method Based on Support Vector Machines (SVM) and Independent Component Analysis (ICA)," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 7(1), pages 12-21, August.
  22. Wietschel, M. & Fichtner, W. & Rentz, O., 1997. "Integration of price-depending demand reactions in an optimising energy emission model for the development of CO2-mitigation strategies," European Journal of Operational Research, Elsevier, vol. 102(3), pages 432-444, November.
  23. Yao, Jingtao & Li, Yili & Tan, Chew Lim, 2000. "Option price forecasting using neural networks," Omega, Elsevier, vol. 28(4), pages 455-466, August.
  24. Andreou, Andreas S. & Zombanakis, George, 2003. "The Greek-Turkish Arms Race Using Artificial Neural Networks," MPRA Paper 78576, University Library of Munich, Germany, revised 14 Jul 2003.
  25. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
  26. Tian, Chaonan & Niu, Tong & Wei, Wei, 2022. "Developing a wind power forecasting system based on deep learning with attention mechanism," Energy, Elsevier, vol. 257(C).
  27. Robert G. Biscontri, 2012. "A Radial Basis Function Approach To Earnings Forecast," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(1), pages 1-18, January.
  28. Ana Lazcano & Pedro Javier Herrera & Manuel Monge, 2023. "A Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series Forecasting," Mathematics, MDPI, vol. 11(1), pages 1-21, January.
  29. Stephan Schlüter & Sejung Jung & Andreas von Döllen & Wonhee Lee, 2022. "An Alternative to Index-Based Gas Sourcing Using Neural Networks," Energies, MDPI, vol. 15(13), pages 1-11, June.
  30. 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.
  31. Jing Wu & Zhaocheng Zhang & Sean X. Zhou, 2022. "Credit Rating Prediction Through Supply Chains: A Machine Learning Approach," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1613-1629, April.
  32. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 672-688, July.
  33. A. S. Andreoua & G. A. Zombanakisb, 2000. "Financial versus human resources in the Greek-Turkish arms race: A forecasting investigation using artificial neural networks," Defence and Peace Economics, Taylor & Francis Journals, vol. 11(2), pages 403-426.
  34. 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.
  35. Hwarng, H. Brian & Ang, H. T., 2001. "A simple neural network for ARMA(p,q) time series," Omega, Elsevier, vol. 29(4), pages 319-333, August.
  36. Edy Fradinata & Sakesun Suthummanon & Wannarat Suntiamorntut, 2015. "Forecasting Determinant of Cement Demand in Indonesia with Artificial Neural Network," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 5(7), pages 373-384, July.
  37. 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.
  38. Yang, Haolin & Schell, Kristen R., 2021. "Real-time electricity price forecasting of wind farms with deep neural network transfer learning and hybrid datasets," Applied Energy, Elsevier, vol. 299(C).
  39. Fernandez, Paula & Teixeira, Joao & Ferreira, Joao & Azevedo, Susana G., 2008. "Modelling Tourism Demand: A Comparative Study Between Artificial Neural Networks And The Box-Jenkins Methodology," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 5(3), pages 30-50, Septembe2.
  40. Long Wen & Chang Liu & Haiyan Song, 2019. "Forecasting tourism demand using search query data: A hybrid modelling approach," Tourism Economics, , vol. 25(3), pages 309-329, May.
  41. Simon Liebermann & Jung-Sup Um & YoungSeok Hwang & Stephan Schlüter, 2021. "Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts," Energies, MDPI, vol. 14(11), pages 1-21, May.
  42. Abdulgani Kahraman & Mehmed Kantardzic & Muhammet Mustafa Kahraman & Muhammed Kotan, 2021. "A Data-Driven Multi-Regime Approach for Predicting Energy Consumption," Energies, MDPI, vol. 14(20), pages 1-17, October.
  43. J V Hansen & R D Nelson, 2003. "Forecasting and recombining time-series components by using neural networks," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(3), pages 307-317, March.
  44. Ana Lazcano & Miguel A. Jaramillo-Morán & Julio E. Sandubete, 2024. "Back to Basics: The Power of the Multilayer Perceptron in Financial Time Series Forecasting," Mathematics, MDPI, vol. 12(12), pages 1-18, June.
  45. Azadeh, A. & Ghaderi, S.F. & Anvari, M. & Saberi, M., 2007. "Performance assessment of electric power generations using an adaptive neural network algorithm," Energy Policy, Elsevier, vol. 35(6), pages 3155-3166, June.
  46. 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.
  47. Waychal, Nachiketas & Laha, Arnab Kumar & Sinha, Ankur, 2022. "Customized forecasting with Adaptive Ensemble Generator," IIMA Working Papers WP 2022-06-04, Indian Institute of Management Ahmedabad, Research and Publication Department.
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