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Machine learning advances for time series forecasting

Citations

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Cited by:

  1. Mario Zupan, 2024. "Accounting journal entries as a long‐term multivariate time series: Forecasting wholesale warehouse output," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(1), March.
  2. Wang, Yijun & Andreeva, Galina & Martin-Barragan, Belen, 2023. "Machine learning approaches to forecasting cryptocurrency volatility: Considering internal and external determinants," International Review of Financial Analysis, Elsevier, vol. 90(C).
  3. Cardo-Miota, Javier & Trivedi, Rohit & Patra, Sandipan & Khadem, Shafi & Bahloul, Mohamed, 2024. "Data-driven approach for day-ahead System Non-Synchronous Penetration forecasting: A comprehensive framework, model development and analysis," Applied Energy, Elsevier, vol. 362(C).
  4. Murat Aslan & Onder Ozgur, 2024. "Financial dollarization and its effects on inflation and output in Turkey: a machine learning approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(6), pages 5777-5804, December.
  5. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
  6. Costola, Michele & Hinz, Oliver & Nofer, Michael & Pelizzon, Loriana, 2023. "Machine learning sentiment analysis, COVID-19 news and stock market reactions," Research in International Business and Finance, Elsevier, vol. 64(C).
  7. Julián Alonso Cárdenas-Cárdenas & Deicy J. Cristiano-Botia & Nicolás Martínez-Cortés, 2023. "Colombian inflation forecast using Long Short-Term Memory approach," Borradores de Economia 1241, Banco de la Republica de Colombia.
  8. Alexandra Bozhechkova & Urmat Dzhunkeev, 2024. "CLARA and CARLSON: Combination of Ensemble and Neural Network Machine Learning Methods for GDP Forecasting," Russian Journal of Money and Finance, Bank of Russia, vol. 83(3), pages 45-69, September.
  9. Pijush Kanti Das & Prabir Kumar Das, 2024. "Forecasting and Analyzing Predictors of Inflation Rate: Using Machine Learning Approach," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 22(2), pages 493-517, June.
  10. Brown, David P. & Cajueiro, Daniel O. & Eckert, Andrew & Silveira, Douglas, 2024. "Evaluating the Role of Information Disclosure on Bidding Behavior in Wholesale Electricity Markets," Working Papers 2024-2, University of Alberta, Department of Economics.
  11. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
  12. Jannet Jamii & Mohamed Trabelsi & Majdi Mansouri & Mohamed Fouazi Mimouni & Wasfi Shatanawi, 2022. "Non-Linear Programming-Based Energy Management for a Wind Farm Coupled with Pumped Hydro Storage System," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
  13. Labib Shami & Teddy Lazebnik, 2024. "Implementing Machine Learning Methods in Estimating the Size of the Non-observed Economy," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1459-1476, April.
  14. Fei Lu & Feng Ma & Elie Bouri, 2024. "Stock market volatility predictability: new evidence from energy consumption," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-17, December.
  15. Felix Chan & Les Oxley, 2023. "A pulse check on recent developments in time series econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 3-6, February.
  16. Zongyu Li & Anmin Zuo & Cuixia Li, 2023. "Predicting Raw Milk Price Based on Depth Time Series Features for Consumer Behavior Analysis," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
  17. Mohitosh Kejriwal & Linh Nguyen & Xuewen Yu, 2023. "Multistep Forecast Averaging with Stochastic and Deterministic Trends," Econometrics, MDPI, vol. 11(4), pages 1-44, December.
  18. Silva, Thiago Christiano & Wilhelm, Paulo Victor Berri & Amancio, Diego R., 2024. "Machine learning and economic forecasting: The role of international trade networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 649(C).
  19. Joao Vitor Matos Goncalves & Michel Alexandre & Gilberto Tadeu Lima, 2023. "ARIMA and LSTM: A Comparative Analysis of Financial Time Series Forecasting," Working Papers, Department of Economics 2023_13, University of São Paulo (FEA-USP).
  20. Mario Figueiredo & Yuri F. Saporito, 2023. "Forecasting the term structure of commodities future prices using machine learning," Digital Finance, Springer, vol. 5(1), pages 57-90, March.
  21. Ariel Alexi & Teddy Lazebnik & Labib Shami, 2024. "Microfounded Tax Revenue Forecast Model with Heterogeneous Population and Genetic Algorithm Approach," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 1705-1734, May.
  22. Urmat Dzhunkeev, 2024. "Forecasting Inflation in Russia Using Gradient Boosting and Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 53-76, March.
  23. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino, 2022. "Forecasting US Inflation Using Bayesian Nonparametric Models," Papers 2202.13793, arXiv.org.
  24. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can Machine Learning Help to Select Portfolios of Mutual Funds?," Working Papers 1245, Barcelona School of Economics.
  25. Chen, Bin & Maung, Kenwin, 2023. "Time-varying forecast combination for high-dimensional data," Journal of Econometrics, Elsevier, vol. 237(2).
  26. Mohammed Baz, 2024. "TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks," Mathematics, MDPI, vol. 12(21), pages 1-33, October.
  27. Hans Genberg & Özer Karagedikli, 2021. "Machine Learning and Central Banks: Ready for Prime Time?," Working Papers wp43, South East Asian Central Banks (SEACEN) Research and Training Centre.
  28. Mahalakshmi Manian & Parthajit Kayal, 2024. "Detecting and Forecasting Financial Bubbles in The Indian Stock Market Using Machine Learning Models," Working Papers 2024-270, Madras School of Economics,Chennai,India.
  29. Yang, Guo-Hui & Zhong, Guang-Yan & Wang, Li-Ya & Xie, Zu-Guang & Li, Jiang-Cheng, 2024. "A hybrid forecasting framework based on MCS and machine learning for higher dimensional and unbalanced systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
  30. Iuri H. Ferreira & Marcelo C. Medeiros, 2021. "Modeling and Forecasting Intraday Market Returns: a Machine Learning Approach," Papers 2112.15108, arXiv.org.
  31. Tostes, Bernardo & Henriques, Sofia T. & Brockway, Paul E. & Heun, Matthew Kuperus & Domingos, Tiago & Sousa, Tânia, 2024. "On the right track? Energy use, carbon emissions, and intensities of world rail transportation, 1840–2020," Applied Energy, Elsevier, vol. 367(C).
  32. Mirna Patricia Ponce-Flores & Jesús David Terán-Villanueva & Salvador Ibarra-Martínez & José Antonio Castán-Rocha, 2023. "Generalized Pandemic Model with COVID-19 for Early-Stage Infection Forecasting," Mathematics, MDPI, vol. 11(18), pages 1-18, September.
  33. Mahmut Bağcı & Pınar Kaya Soylu & Selçuk Kıran, 2024. "The Symmetric and Asymmetric Algorithmic Trading Strategies for the Stablecoins," Computational Economics, Springer;Society for Computational Economics, vol. 64(5), pages 2663-2684, November.
  34. Moreno, Sinvaldo Rodrigues & Seman, Laio Oriel & Stefenon, Stefano Frizzo & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2024. "Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition," Energy, Elsevier, vol. 292(C).
  35. Chu Myaet Thwal & Ye Lin Tun & Kitae Kim & Seong-Bae Park & Choong Seon Hong, 2024. "Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting," Papers 2402.06638, arXiv.org.
  36. Kexin Zhang & Simon Trimborn, 2024. "Influential assets in Large-Scale Vector AutoRegressive Models," Tinbergen Institute Discussion Papers 24-080/III, Tinbergen Institute.
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