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Forecasting with artificial neural networks:: The state of the art

Citations

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Cited by:

  1. Jonathan Fabián Dato & Matías Gabriel Dinápoli & Enrique Eduardo D’Onofrio & Claudia Gloria Simionato, 2024. "On water level forecasting using artificial neural networks: the case of the Río de la Plata Estuary, Argentina," 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. 120(11), pages 9753-9776, September.
  2. Jha, Girish K. & Sinha, Kanchan, 2013. "Agricultural Price Forecasting Using Neural Network Model: An Innovative Information Delivery System," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 26(2).
  3. Jichang Dong & Wei Dai & Ying Liu & Lean Yu & Jie Wang, 2019. "Forecasting Chinese Stock Market Prices using Baidu Search Index with a Learning-Based Data Collection Method," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1605-1629, September.
  4. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
  5. Golnoosh Babaei & Shahrooz Bamdad, 2021. "A New Hybrid Instance-Based Learning Model for Decision-Making in the P2P Lending Market," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 419-432, January.
  6. Guoming Wang & Woo-Hyun Kim & Gyung-Suk Kil & Dae-Won Park & Sung-Wook Kim, 2019. "An Intelligent Lightning Warning System Based on Electromagnetic Field and Neural Network," Energies, MDPI, vol. 12(7), pages 1-11, April.
  7. Sen Cheong Kon & Lindsay W. Turner, 2005. "Neural Network Forecasting of Tourism Demand," Tourism Economics, , vol. 11(3), pages 301-328, September.
  8. Christoph Gleue & Dennis Eilers & Hans-Jörg Mettenheim & Michael H. Breitner, 2019. "Decision Support for the Automotive Industry," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(4), pages 385-397, August.
  9. Lakhwinder Pal Singh & Ravi Teja Challa, 2016. "Integrated Forecasting Using the Discrete Wavelet Theory and Artificial Intelligence Techniques to Reduce the Bullwhip Effect in a Supply Chain," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 17(2), pages 157-169, June.
  10. Balkin, Sandy, 2001. "On Forecasting Exchange Rates Using Neural Networks: P.H. Franses and P.V. Homelen, 1998, Applied Financial Economics, 8, 589-596," International Journal of Forecasting, Elsevier, vol. 17(1), pages 139-140.
  11. 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.
  12. Prashant K. Srivastava & Manika Gupta & Ujjwal Singh & Rajendra Prasad & Prem Chandra Pandey & A. S. Raghubanshi & George P. Petropoulos, 2021. "Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 5504-5519, April.
  13. Rakesh K. Bissoondeeal & Michail Karoglou & Alicia M. Gazely, 2011. "Forecasting The Uk/Us Exchange Rate With Divisia Monetary Models And Neural Networks," Scottish Journal of Political Economy, Scottish Economic Society, vol. 58(1), pages 127-152, February.
  14. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
  15. Soheila Khajoui & Saeid Dehyadegari & Sayyed Abdolmajid Jalaee, 2024. "Predicting the impact of e-commerce indices on international trade in Iran and other selected members of the Organization for Economic Co-operation and Development (OECD) by using the artificial intel," Papers 2403.20310, arXiv.org.
  16. Laborda, Ricardo & Laborda, Juan, 2017. "Can tree-structured classifiers add value to the investor?," Finance Research Letters, Elsevier, vol. 22(C), pages 211-226.
  17. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
  18. Nur Rahman & Muhammad Lee & Suhartono & Mohd Latif, 2015. "Artificial neural networks and fuzzy time series forecasting: an application to air quality," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(6), pages 2633-2647, November.
  19. Chiroma, Haruna & Abdulkareem, Sameem & Herawan, Tutut, 2015. "Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction," Applied Energy, Elsevier, vol. 142(C), pages 266-273.
  20. Zhang, Jianhong & van Witteloostuijn, Arjen & Zhou, Chaohong & Zhou, Shengyang, 2024. "Cross-border acquisition completion by emerging market MNEs revisited: Inductive evidence from a machine learning analysis," Journal of World Business, Elsevier, vol. 59(2).
  21. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
  22. Au, Kin-Fan & Choi, Tsan-Ming & Yu, Yong, 2008. "Fashion retail forecasting by evolutionary neural networks," International Journal of Production Economics, Elsevier, vol. 114(2), pages 615-630, August.
  23. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
  24. Cagdas Hakan Aladag & Ufuk Yolcu & Erol Egrioglu & I. Burhan Turksen, 2016. "Type-1 fuzzy time series function method based on binary particle swarm optimisation," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 8(1), pages 2-13.
  25. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
  26. Evangelos Spiliotis & Fotios Petropoulos & Konstantinos Nikolopoulos, 2020. "The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece," Energies, MDPI, vol. 13(8), pages 1-18, April.
  27. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
  28. 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.
  29. Francisco Salas-Molina & Juan A. Rodr'iguez-Aguilar & Joan Serr`a & Montserrat Guillen & Francisco J. Martin, 2016. "Empirical analysis of daily cash flow time series and its implications for forecasting," Papers 1611.04941, arXiv.org, revised Jun 2017.
  30. Peng Zhu & Yuante Li & Yifan Hu & Qinyuan Liu & Dawei Cheng & Yuqi Liang, 2024. "LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU," Papers 2409.08282, arXiv.org, revised Sep 2024.
  31. Witte, Björn-Christopher, 2011. "Removing systematic patterns in returns in a financial market model by artificially intelligent traders," BERG Working Paper Series 82, Bamberg University, Bamberg Economic Research Group.
  32. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos, 2021. "Trading the foreign exchange market with technical analysis and Bayesian Statistics," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 230-251.
  33. Khan, Muhammad Asif & Segovia, Juan E.Trinidad & Bhatti, M.Ishaq & Kabir, Asif, 2023. "Corporate vulnerability in the US and China during COVID-19: A machine learning approach," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).
  34. Faisal Khalil & Gordon Pipa, 2022. "Is Deep-Learning and Natural Language Processing Transcending the Financial Forecasting? Investigation Through Lens of News Analytic Process," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 147-171, June.
  35. Poornima Unnikrishnan & V. Jothiprakash, 2020. "Hybrid SSA-ARIMA-ANN Model for Forecasting Daily Rainfall," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3609-3623, September.
  36. Bessec, Marie & Fouquau, Julien, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," European Journal of Operational Research, Elsevier, vol. 264(1), pages 149-164.
  37. James R. Coakley & Carol E. Brown, 2000. "Artificial neural networks in accounting and finance: modeling issues," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(2), pages 119-144, June.
  38. Roman Trach & Yuliia Trach & Agnieszka Kiersnowska & Anna Markiewicz & Marzena Lendo-Siwicka & Konstantin Rusakov, 2022. "A Study of Assessment and Prediction of Water Quality Index Using Fuzzy Logic and ANN Models," Sustainability, MDPI, vol. 14(9), pages 1-19, May.
  39. Stepchenko Arthur & Borisov Arkady, 2014. "Methods of Forecasting Based on Artificial Neural Networks/ Prognozēšanas metodes, kas balstītas uz mākslīgajiem neironu tīkliem/ Методы прогнозирования, основанные на искусственных нейронных сетях," Information Technology and Management Science, Sciendo, vol. 17(1), pages 25-31, December.
  40. Mengyang Wang & Hui Wang & Jiao Wang & Hongwei Liu & Rui Lu & Tongqing Duan & Xiaowen Gong & Siyuan Feng & Yuanyuan Liu & Zhuang Cui & Changping Li & Jun Ma, 2019. "A novel model for malaria prediction based on ensemble algorithms," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-15, December.
  41. Darbellay, Georges A. & Slama, Marek, 2000. "Forecasting the short-term demand for electricity: Do neural networks stand a better chance?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 71-83.
  42. Trong‐Nghia Nguyen & Minh‐Ngoc Tran & Robert Kohn, 2022. "Recurrent conditional heteroskedasticity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1031-1054, August.
  43. 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.
  44. 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.
  45. Goutam Dutta & Pankaj Jha & Arnab Kumar Laha & Neeraj Mohan, 2006. "Artificial Neural Network Models for Forecasting Stock Price Index in the Bombay Stock Exchange," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 5(3), pages 283-295, December.
  46. Guallar, Carles & Delgado, Maximino & Diogène, Jorge & Fernández-Tejedor, Margarita, 2016. "Artificial neural network approach to population dynamics of harmful algal blooms in Alfacs Bay (NW Mediterranean): Case studies of Karlodinium and Pseudo-nitzschia," Ecological Modelling, Elsevier, vol. 338(C), pages 37-50.
  47. Hu, Michael Y. & Tsoukalas, Christos, 1999. "Combining conditional volatility forecasts using neural networks: an application to the EMS exchange rates," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 9(4), pages 407-422, November.
  48. Bekiros, Stelios D., 2015. "Heuristic learning in intraday trading under uncertainty," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 34-49.
  49. Fu, Sibao & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2019. "Evolutionary support vector machine for RMB exchange rate forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 692-704.
  50. Salim Lahmiri, 2017. "A two‐step system for direct bank telemarketing outcome classification," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 24(1), pages 49-55, January.
  51. Khurshid Kiani, 2011. "Fluctuations in Economic and Activity and Stabilization Policies in the CIS," Computational Economics, Springer;Society for Computational Economics, vol. 37(2), pages 193-220, February.
  52. Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2011. "Neural networks for regional employment forecasts: are the parameters relevant?," Journal of Geographical Systems, Springer, vol. 13(1), pages 67-85, March.
  53. Jesús Manuel De Sancha-Navarro & Juan Lara-Rubio & María Dolores Oliver-Alfonso & Luis Palma-Martos, 2021. "Cultural Sustainability in University Students’ Flamenco Music Event Attendance: A Neural Networks Approach," Sustainability, MDPI, vol. 13(5), pages 1-15, March.
  54. Huck, Nicolas, 2010. "Pairs trading and outranking: The multi-step-ahead forecasting case," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1702-1716, December.
  55. Xiao Zhong & David Enke, 2019. "Predicting the daily return direction of the stock market using hybrid machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-20, December.
  56. 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.
  57. Amuktamalyada Gorlapalli & Supriya Kallakuri & Pagadala Damodaram Sreekanth & Rahul Patil & Nirmala Bandumula & Gabrijel Ondrasek & Meena Admala & Channappa Gireesh & Madhyavenkatapura Siddaiah Ananth, 2022. "Characterization and Prediction of Water Stress Using Time Series and Artificial Intelligence Models," Sustainability, MDPI, vol. 14(11), pages 1-18, May.
  58. Naif Radi Aljohani & Ayman Fayoumi & Saeed-Ul Hassan, 2019. "Predicting At-Risk Students Using Clickstream Data in the Virtual Learning Environment," Sustainability, MDPI, vol. 11(24), pages 1-12, December.
  59. Moreno, David & Olmeda, Ignacio, 2007. "Is the predictability of emerging and developed stock markets really exploitable?," European Journal of Operational Research, Elsevier, vol. 182(1), pages 436-454, October.
  60. Khurshid M. KIANI & Terry L. KASTENS, 2006. "Using Macro-Financial Variables To Forecast Recessions. An Analysis Of Canada, 1957-2002," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 6(3).
  61. Lorena Yepes-Bellver & Alejandro Brun-Izquierdo & Julián Alcalá & Víctor Yepes, 2024. "Artificial Neural Network and Kriging Surrogate Model for Embodied Energy Optimization of Prestressed Slab Bridges," Sustainability, MDPI, vol. 16(19), pages 1-14, September.
  62. G. Peter Zhang & Yusen Xia & Maohua Xie, 2024. "Intermittent demand forecasting with transformer neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 1051-1072, August.
  63. Aastha M. Sathe & Neelesh S. Upadhye & Agnieszka Wyłomańska, 2024. "Forecasting multidimensional autoregressive time series model with symmetric $$\alpha$$ α -stable noise using artificial neural networks," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(3), pages 783-805, July.
  64. Hasnain Iftikhar & Nadeela Bibi & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan," Energies, MDPI, vol. 16(6), pages 1-17, March.
  65. X. Ajay Vasanth & P. Sam Paul & A. S. Varadarajan, 0. "A neural network model to predict surface roughness during turning of hardened SS410 steel," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 0, pages 1-12.
  66. Jin, Feng & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2020. "Forecasting air passenger demand with a new hybrid ensemble approach," Journal of Air Transport Management, Elsevier, vol. 83(C).
  67. Oscar Claveria & Enric Monte & Salvador Torra, 2014. "“A multivariate neural network approach to tourism demand forecasting”," IREA Working Papers 201417, University of Barcelona, Research Institute of Applied Economics, revised May 2014.
  68. Viacheslav Morozov & Artur I. Petrov & Vladimir Shepelev & Mohammed Balfaqih, 2024. "Ideology of Urban Road Transport Chaos and Accident Risk Management for Sustainable Transport Systems," Sustainability, MDPI, vol. 16(6), pages 1-32, March.
  69. 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.
  70. 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.
  71. Kourentzes, Nikolaos, 2013. "Intermittent demand forecasts with neural networks," International Journal of Production Economics, Elsevier, vol. 143(1), pages 198-206.
  72. Maheronnaghsh, Mohammad Javad & Gheidi, Mohammad Mahdi & Fazli, MohammadAmin, 2023. "Machine Learning Methods in Algorithmic Trading: An Experimental Evaluation of Supervised Learning Techniques for Stock Price," OSF Preprints dzp26, Center for Open Science.
  73. Huber, Jakob & Müller, Sebastian & Fleischmann, Moritz & Stuckenschmidt, Heiner, 2019. "A data-driven newsvendor problem: From data to decision," European Journal of Operational Research, Elsevier, vol. 278(3), pages 904-915.
  74. Wang, Lijun & An, Haizhong & Liu, Xiaojia & Huang, Xuan, 2016. "Selecting dynamic moving average trading rules in the crude oil futures market using a genetic approach," Applied Energy, Elsevier, vol. 162(C), pages 1608-1618.
  75. Salim Djerbouai & Doudja Souag-Gamane, 2016. "Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Stochastic Models: Case of the Algerois Basin in North Algeria," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(7), pages 2445-2464, May.
  76. Tkacz, Greg, 2001. "Neural network forecasting of Canadian GDP growth," International Journal of Forecasting, Elsevier, vol. 17(1), pages 57-69.
  77. Singh, Shiwangi & Dhir, Sanjay & Das, V. Mukunda & Sharma, Anuj, 2020. "Bibliometric overview of the Technological Forecasting and Social Change journal: Analysis from 1970 to 2018," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
  78. Blerina Vika & Kozeta Sevrani & Ilir Vika, 2016. "The Usefulness of Artificial Neural Networks in Forecasting Exchange Rates," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 5, March.
  79. Tapia, Sebastian & Kristjanpoller, Werner, 2022. "Framework based on multiplicative error and residual analysis to forecast bitcoin intraday-volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
  80. Georgios N. Kouziokas & Alexander Chatzigeorgiou & Konstantinos Perakis, 2018. "Multilayer Feed Forward Models in Groundwater Level Forecasting Using Meteorological Data in Public Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 5041-5052, December.
  81. Sulandari, Winita & Subanar, & Lee, Muhammad Hisyam & Rodrigues, Paulo Canas, 2020. "Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks," Energy, Elsevier, vol. 190(C).
  82. Saeed Azimi & Mehdi Azhdary Moghaddam, 2020. "Modeling Short Term Rainfall Forecast Using Neural Networks, and Gaussian Process Classification Based on the SPI Drought Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(4), pages 1369-1405, March.
  83. Adedayo Ajayi & Patrick Chi-Kwong Luk & Liyun Lao & Mohammad Farhan Khan, 2023. "Energy Forecasting Model for Ground Movement Operation in Green Airport," Energies, MDPI, vol. 16(13), pages 1-19, June.
  84. J. Lara‐Rubio & A. Blanco‐Oliver & R. Pino‐Mejías, 2017. "Promoting Entrepreneurship at the Base of the Social Pyramid via Pricing Systems: A case Study," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 24(1), pages 12-28, January.
  85. Sermpinis, Georgios & Theofilatos, Konstantinos & Karathanasopoulos, Andreas & Georgopoulos, Efstratios F. & Dunis, Christian, 2013. "Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization," European Journal of Operational Research, Elsevier, vol. 225(3), pages 528-540.
  86. Vincenzo Candila & Lucio Palazzo, 2020. "Neural Networks and Betting Strategies for Tennis," Risks, MDPI, vol. 8(3), pages 1-19, June.
  87. 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.
  88. 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.
  89. Luna, Ivette & Ballini, Rosangela, 2011. "Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 708-724, July.
  90. Huck, Nicolas, 2009. "Pairs selection and outranking: An application to the S&P 100 index," European Journal of Operational Research, Elsevier, vol. 196(2), pages 819-825, July.
  91. Harish Kumar Ghritlahre & Purvi Chandrakar & Ashfaque Ahmad, 2021. "A Comprehensive Review on Performance Prediction of Solar Air Heaters Using Artificial Neural Network," Annals of Data Science, Springer, vol. 8(3), pages 405-449, September.
  92. Feng Xu & Mohamad Sepehri & Jian Hua & Sergey Ivanov & Julius N. Anyu, 2018. "Time-Series Forecasting Models for Gasoline Prices in China," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(12), pages 1-43, December.
  93. Daniel Santin & Francisco Delgado & Aurelia Valino, 2004. "The measurement of technical efficiency: a neural network approach," Applied Economics, Taylor & Francis Journals, vol. 36(6), pages 627-635.
  94. Wang, Chen & He, Qijiao & Li, Zheng & Yu, Jie & Bello, Idris Temitope & Zheng, Keqing & Han, Minfang & Ni, Meng, 2024. "A novel in-tube reformer for solid oxide fuel cell for performance improvement and efficient thermal management: A numerical study based on artificial neural network and genetic algorithm," Applied Energy, Elsevier, vol. 357(C).
  95. Yolcu Ufuk & Bas Eren, 2016. "The Forecasting of Labour Force Participation and the Unemployment Rate in Poland and Turkey Using Fuzzy Time Series Methods," Comparative Economic Research, Sciendo, vol. 19(2), pages 5-25, June.
  96. Adam P. Piotrowski & Maciej J. Napiorkowski & Monika Kalinowska & Jaroslaw J. Napiorkowski & Marzena Osuch, 2016. "Are Evolutionary Algorithms Effective in Calibrating Different Artificial Neural Network Types for Streamwater Temperature Prediction?," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1217-1237, February.
  97. Shi, Yu & Song, Xianzhi & Song, Guofeng, 2021. "Productivity prediction of a multilateral-well geothermal system based on a long short-term memory and multi-layer perceptron combinational neural network," Applied Energy, Elsevier, vol. 282(PA).
  98. Wei Sun & Yujun He & Hong Chang, 2015. "Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model," Energies, MDPI, vol. 8(2), pages 1-21, January.
  99. Jun Kang & Hyun Jun Lee & Seung Hwan Jeong & Hee Soo Lee & Kyong Joo Oh, 2020. "Developing a Forecasting Model for Real Estate Auction Prices Using Artificial Intelligence," Sustainability, MDPI, vol. 12(7), pages 1-19, April.
  100. Chenghao Zhong & Wengao Lou & Chuting Wang, 2022. "Neural Network-Based Modeling for Risk Evaluation and Early Warning for Large-Scale Sports Events," Mathematics, MDPI, vol. 10(18), pages 1-16, September.
  101. Abdoulaye Camara & Wang Feixing & Liu Xiuqin, 2016. "Energy Consumption Forecasting Using Seasonal ARIMA with Artificial Neural Networks Models," International Journal of Business and Management, Canadian Center of Science and Education, vol. 11(5), pages 231-231, April.
  102. M. Reza Hashemi & Malcolm L. Spaulding & Alex Shaw & Hamed Farhadi & Matt Lewis, 2016. "An efficient artificial intelligence model for prediction of tropical storm surge," 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. 82(1), pages 471-491, May.
  103. Samya Tajmouati & Bouazza El Wahbi & Mohamed Dakkon, 2023. "Classical and fast parameters tuning in nearest neighbors with stop condition," OPSEARCH, Springer;Operational Research Society of India, vol. 60(3), pages 1063-1081, September.
  104. Shin, Ki-Hong & Baek, Woonhak & Kim, Kyungsik & You, Cheol-Hwan & Chang, Ki-Ho & Lee, Dong-In & Yum, Seong Soo, 2019. "Neural network and regression methods for optimizations between two meteorological factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 778-796.
  105. Schnaubelt, Matthias, 2022. "Deep reinforcement learning for the optimal placement of cryptocurrency limit orders," European Journal of Operational Research, Elsevier, vol. 296(3), pages 993-1006.
  106. Elías, Antonio & Jiménez, Raúl & Shang, Han Lin, 2022. "On projection methods for functional time series forecasting," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
  107. Parmar, Janak & Das, Pritikana & Dave, Sanjaykumar M., 2021. "A machine learning approach for modelling parking duration in urban land-use," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
  108. Firdous Ahmad Shah & Lokenath Debnath, 2017. "Wavelet Neural Network Model for Yield Spread Forecasting," Mathematics, MDPI, vol. 5(4), pages 1-15, November.
  109. Saman, Corina, 2011. "Scenarios of the Romanian GDP Evolution With Neural Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 129-140, December.
  110. Qing Cao & Mark Parry & Karyl Leggio, 2011. "The three-factor model and artificial neural networks: predicting stock price movement in China," Annals of Operations Research, Springer, vol. 185(1), pages 25-44, May.
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