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An Empirical Comparison of Machine Learning Models for Time Series Forecasting

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  1. 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).
  2. Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022. "“Density forecasts of inflation using Gaussian process regression models”," AQR Working Papers 202207, University of Barcelona, Regional Quantitative Analysis Group, revised Jul 2022.
  3. Ulrich, Matthias & Jahnke, Hermann & Langrock, Roland & Pesch, Robert & Senge, Robin, 2022. "Classification-based model selection in retail demand forecasting," International Journal of Forecasting, Elsevier, vol. 38(1), pages 209-223.
  4. George Fragiadakis & Evangelia Filiopoulou & Christos Michalakelis & Thomas Kamalakis & Mara Nikolaidou, 2023. "Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS," Future Internet, MDPI, vol. 15(8), pages 1-19, August.
  5. Bartram, Söhnke & Branke, Jürgen & Motahari, Mehrshad, 2020. "Artificial Intelligence in Asset Management," CEPR Discussion Papers 14525, C.E.P.R. Discussion Papers.
  6. Ben Moews & J. Michael Herrmann & Gbenga Ibikunle, 2018. "Lagged correlation-based deep learning for directional trend change prediction in financial time series," Papers 1811.11287, arXiv.org, revised Nov 2018.
  7. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
  8. Kock, Anders Bredahl & Teräsvirta, Timo, 2014. "Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009," International Journal of Forecasting, Elsevier, vol. 30(3), pages 616-631.
  9. Anders Bredahl Kock & Timo Teräsvirta, 2016. "Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1753-1779, December.
  10. 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.
  11. Tianxiang Zhan & Fuyuan Xiao, 2021. "A Fast Evidential Approach for Stock Forecasting," Papers 2104.05204, arXiv.org, revised Jul 2021.
  12. Maghsoodi, Abtin Ijadi, 2023. "Cryptocurrency portfolio allocation using a novel hybrid and predictive big data decision support system," Omega, Elsevier, vol. 115(C).
  13. Tussyadiah, Iis, 2020. "A review of research into automation in tourism: Launching the Annals of Tourism Research Curated Collection on Artificial Intelligence and Robotics in Tourism," Annals of Tourism Research, Elsevier, vol. 81(C).
  14. Twumasi, Clement & Twumasi, Juliet, 2022. "Machine learning algorithms for forecasting and backcasting blood demand data with missing values and outliers: A study of Tema General Hospital of Ghana," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1258-1277.
  15. 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.
  16. Ran-Ran He & Yuanfang Chen & Qin Huang & Zheng-Wei Pan & Yong Liu, 2020. "Predictability of Monthly Streamflow Time Series and its Relationship with Basin Characteristics: an Empirical Study Based on the MOPEX Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4991-5007, December.
  17. Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021. "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, vol. 37(2), pages 941-948.
  18. Onur Enginar & Kazim Baris Atici, 2022. "Optimal forecast error as an unbiased estimator of abnormal return: A proposition," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 158-166, January.
  19. Akın, Melda, 2015. "A novel approach to model selection in tourism demand modeling," Tourism Management, Elsevier, vol. 48(C), pages 64-72.
  20. Stolbunov, Valentin & Nair, Prasanth B., 2018. "Sparse radial basis function approximation with spatially variable shape parameters," Applied Mathematics and Computation, Elsevier, vol. 330(C), pages 170-184.
  21. Xin Huang & Han Lin Shang & David Pitt, 2022. "A model sufficiency test using permutation entropy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 1017-1036, August.
  22. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2020. "Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting," Energies, MDPI, vol. 13(2), pages 1-21, January.
  23. Brummelhuis, Raymond & Luo, Zhongmin, 2019. "Bank Net Interest Margin Forecasting and Capital Adequacy Stress Testing by Machine Learning Techniques," MPRA Paper 94779, University Library of Munich, Germany.
  24. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "“Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting"," IREA Working Papers 201701, University of Barcelona, Research Institute of Applied Economics, revised Jan 2017.
  25. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
  26. Wu, Shaomin & Akbarov, Artur, 2011. "Support vector regression for warranty claim forecasting," European Journal of Operational Research, Elsevier, vol. 213(1), pages 196-204, August.
  27. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
  28. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2018. "The M4 Competition: Results, findings, conclusion and way forward," International Journal of Forecasting, Elsevier, vol. 34(4), pages 802-808.
  29. Li, Danny H.W. & Aghimien, Emmanuel I. & Tsang, Ernest K.W., 2022. "Application of artificial neural networks in horizontal luminous efficacy modeling," Renewable Energy, Elsevier, vol. 197(C), pages 864-878.
  30. Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021. "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, vol. 37(2), pages 941-948.
  31. Neil Kellard & Denise Osborn & Jerry Coakley & Imanol Arrieta-ibarra & Ignacio N. Lobato, 2015. "Testing for Predictability in Financial Returns Using Statistical Learning Procedures," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 672-686, September.
  32. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
  33. Siva R Venna & Satya Katragadda & Vijay Raghavan & Raju Gottumukkala, 2021. "River Stage Forecasting using Enhanced Partial Correlation Graph," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4111-4126, September.
  34. Zaher Mundher Yaseen & Mazen Ismaeel Ghareb & Isa Ebtehaj & Hossein Bonakdari & Ridwan Siddique & Salim Heddam & Ali A. Yusif & Ravinesh Deo, 2018. "Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 105-122, January.
  35. Lake, A., 2020. "Optimal Feasible Expectations in Economics and Finance," Cambridge Working Papers in Economics 20105, Faculty of Economics, University of Cambridge.
  36. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
  37. Martins, Guilherme Santos & Giesbrecht, Mateus, 2021. "Clearness index forecasting: A comparative study between a stochastic realization method and a machine learning algorithm," Renewable Energy, Elsevier, vol. 180(C), pages 787-805.
  38. Robaina, M. & Madaleno, M. & Silva, S. & Eusébio, C. & Carneiro, M.J. & Gama, C. & Oliveira, K. & Russo, M.A. & Monteiro, A., 2020. "The relationship between tourism and air quality in five European countries," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 261-272.
  39. Soyeon Caren Han & Yulu Liang & Hyunsuk Chung & Hyejin Kim & Byeong Ho Kang, 2016. "Chinese trending search terms popularity rank prediction," Information Technology and Management, Springer, vol. 17(2), pages 133-139, June.
  40. Green, Gareth & Richards, Timothy, 2016. "Interpreting Results of Demand Estimation from Machine Learning Models," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236147, Agricultural and Applied Economics Association.
  41. Cui, Can & Wu, Teresa & Hu, Mengqi & Weir, Jeffery D. & Li, Xiwang, 2016. "Short-term building energy model recommendation system: A meta-learning approach," Applied Energy, Elsevier, vol. 172(C), pages 251-263.
  42. Gert Bijnens & Shyngys Karimov & Jozef Konings, 2023. "Does Automatic Wage Indexation Destroy Jobs? A Machine Learning Approach," De Economist, Springer, vol. 171(1), pages 85-117, March.
  43. Ballı, Serkan, 2021. "Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
  44. Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022. "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper 441, CPB Netherlands Bureau for Economic Policy Analysis.
  45. Wong, Melvin & Farooq, Bilal & Bilodeau, Guillaume-Alexandre, 2018. "Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling," Journal of choice modelling, Elsevier, vol. 29(C), pages 152-168.
  46. Tanujit Chakraborty & Ashis Kumar Chakraborty & Munmun Biswas & Sayak Banerjee & Shramana Bhattacharya, 2021. "Unemployment Rate Forecasting: A Hybrid Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 183-201, January.
  47. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
  48. Ziras, Charalampos & Heinrich, Carsten & Pertl, Michael & Bindner, Henrik W., 2019. "Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data," Applied Energy, Elsevier, vol. 242(C), pages 1407-1421.
  49. Robert RUSU & Constantin AVRAM, 2022. "Deep Learning Systems Integrated into the Digital Strategy of a Company Involved in e-commerce," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 1, pages 5-10.
  50. Haoran Wang & Shi Yu, 2021. "Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning," Papers 2105.09264, arXiv.org.
  51. Semenoglou, Artemios-Anargyros & Spiliotis, Evangelos & Makridakis, Spyros & Assimakopoulos, Vassilios, 2021. "Investigating the accuracy of cross-learning time series forecasting methods," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1072-1084.
  52. 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.
  53. Ortega, Luz C. & Otero, Luis Daniel & Solomon, Mitchell & Otero, Carlos E. & Fabregas, Aldo, 2023. "Deep learning models for visibility forecasting using climatological data," International Journal of Forecasting, Elsevier, vol. 39(2), pages 992-1004.
  54. Marco S. Reis & Ricardo Rendall & Biagio Palumbo & Antonio Lepore & Christian Capezza, 2020. "Predicting ships' CO2 emissions using feature‐oriented methods," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(1), pages 110-123, January.
  55. Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022. "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers 2209.09649, arXiv.org, revised Jul 2023.
  56. Qi Tang & Tongmei Fan & Ruchen Shi & Jingyan Huang & Yidan Ma, 2021. "Prediction of financial time series using LSTM and data denoising methods," Papers 2103.03505, arXiv.org.
  57. Baumann, P. & Hochbaum, D.S. & Yang, Y.T., 2019. "A comparative study of the leading machine learning techniques and two new optimization algorithms," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1041-1057.
  58. Advait Sarkar & Neal Lathia & Cecilia Mascolo, 2015. "Comparing cities’ cycling patterns using online shared bicycle maps," Transportation, Springer, vol. 42(4), pages 541-559, July.
  59. Koffi, Siméon, 2022. "Prévision de l’inflation en Côte D’ivoire : Analyse Comparée des Modèles Arima, Holt-Winters, et Lstm [Inflation Forecasting in Côte D'Ivoire: A Comparative Analysis of the Arima, Holt-Winters, and," MPRA Paper 113961, University Library of Munich, Germany.
  60. Anna Almosova & Niek Andresen, 2023. "Nonlinear inflation forecasting with recurrent neural networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 240-259, March.
  61. Ioannis Papageorgiou & Ioannis Kontoyiannis, 2023. "The Bayesian Context Trees State Space Model for time series modelling and forecasting," Papers 2308.00913, arXiv.org, revised Oct 2023.
  62. Arnab Mitra & Arnav Jain & Avinash Kishore & Pravin Kumar, 2022. "A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach," SN Operations Research Forum, Springer, vol. 3(4), pages 1-22, December.
  63. Michael R. Johnson & Hiten Naik & Wei Siang Chan & Jesse Greiner & Matt Michaleski & Dong Liu & Bruno Silvestre & Ian P. McCarthy, 2023. "Forecasting ward-level bed requirements to aid pandemic resource planning: Lessons learned and future directions," Health Care Management Science, Springer, vol. 26(3), pages 477-500, September.
  64. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“A regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics”," IREA Working Papers 201805, University of Barcelona, Research Institute of Applied Economics, revised Mar 2018.
  65. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
  66. Tzai-Shuen Chen, 2018. "Evaluating Conditional Cash Transfer Policies with Machine Learning Methods," Papers 1803.06401, arXiv.org.
  67. Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
  68. Indy Man Kit Ho & Kai Yuen Cheong & Anthony Weldon, 2021. "Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-27, April.
  69. Dietrich, Bastian & Walther, Jessica & Weigold, Matthias & Abele, Eberhard, 2020. "Machine learning based very short term load forecasting of machine tools," Applied Energy, Elsevier, vol. 276(C).
  70. David Bienvenido-Huertas & Jesús A. Pulido-Arcas & Carlos Rubio-Bellido & Alexis Pérez-Fargallo, 2021. "Prediction of Fuel Poverty Potential Risk Index Using Six Regression Algorithms: A Case-Study of Chilean Social Dwellings," Sustainability, MDPI, vol. 13(5), pages 1-30, February.
  71. Silvia Golia & Luigi Grossi & Matteo Pelagatti, 2022. "Machine Learning Models and Intra-Daily Market Information for the Prediction of Italian Electricity Prices," Forecasting, MDPI, vol. 5(1), pages 1-21, December.
  72. Karol Bot & Samira Santos & Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano, 2021. "Design of Ensemble Forecasting Models for Home Energy Management Systems," Energies, MDPI, vol. 14(22), pages 1-37, November.
  73. Huber, Jakob & Stuckenschmidt, Heiner, 2021. "Intraday shelf replenishment decision support for perishable goods," International Journal of Production Economics, Elsevier, vol. 231(C).
  74. Meenakshi Narayan & Ann Majewicz Fey, 2020. "Developing a novel force forecasting technique for early prediction of critical events in robotics," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-34, May.
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