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Recurrent Neural Networks for Time Series Forecasting: Current status and future directions

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  1. Janczura, Joanna & Wójcik, Edyta, 2022. "Dynamic short-term risk management strategies for the choice of electricity market based on probabilistic forecasts of profit and risk measures. The German and the Polish market case study," Energy Economics, Elsevier, vol. 110(C).
  2. Fang, Lei & He, Bin, 2023. "A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting," Applied Energy, Elsevier, vol. 348(C).
  3. Miguel Núñez-Peiró & Anna Mavrogianni & Phil Symonds & Carmen Sánchez-Guevara Sánchez & F. Javier Neila González, 2021. "Modelling Long-Term Urban Temperatures with Less Training Data: A Comparative Study Using Neural Networks in the City of Madrid," Sustainability, MDPI, vol. 13(15), pages 1-23, July.
  4. Montero-Manso, Pablo & Hyndman, Rob J., 2021. "Principles and algorithms for forecasting groups of time series: Locality and globality," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1632-1653.
  5. Saidjon Shiralievich Tavarov & Alexander Sidorov & Zsolt Čonka & Murodbek Safaraliev & Pavel Matrenin & Mihail Senyuk & Svetlana Beryozkina & Inga Zicmane, 2023. "Control of Operational Modes of an Urban Distribution Grid under Conditions of Uncertainty," Energies, MDPI, vol. 16(8), pages 1-18, April.
  6. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
  7. Hanif, M.F. & Mi, J., 2024. "Harnessing AI for solar energy: Emergence of transformer models," Applied Energy, Elsevier, vol. 369(C).
  8. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "Predicting/hypothesizing the findings of the M5 competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1337-1345.
  9. Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. "“An application of deep learning for exchange rate forecasting”," AQR Working Papers 202201, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2022.
  10. Ramos, Paulo Vitor B. & Villela, Saulo Moraes & Silva, Walquiria N. & Dias, Bruno H., 2023. "Residential energy consumption forecasting using deep learning models," Applied Energy, Elsevier, vol. 350(C).
  11. Vuong, Van-Dai & Nguyen, Luong-Ha & Goulet, James-A., 2025. "Coupling LSTM neural networks and state-space models through analytically tractable inference," International Journal of Forecasting, Elsevier, vol. 41(1), pages 128-140.
  12. Lena Sasal & Tanujit Chakraborty & Abdenour Hadid, 2022. "W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting," Papers 2209.03945, arXiv.org.
  13. Dai, Xiaoran & Liu, Guo-Ping & Hu, Wenshan, 2023. "An online-learning-enabled self-attention-based model for ultra-short-term wind power forecasting," Energy, Elsevier, vol. 272(C).
  14. Wellens, Arnoud P. & Boute, Robert N. & Udenio, Maximiliano, 2024. "Simplifying tree-based methods for retail sales forecasting with explanatory variables," European Journal of Operational Research, Elsevier, vol. 314(2), pages 523-539.
  15. Mao, Xuehui & Chen, Shanlin & Yu, Hanxin & Duan, Liwu & He, Yingjie & Chu, Yinghao, 2025. "Simplicity in dynamic and competitive electricity markets: A case study on enhanced linear models versus complex deep-learning models for day-ahead electricity price forecasting," Applied Energy, Elsevier, vol. 383(C).
  16. Saurabh Kamal & Sahil Sharma & Vijay Kumar & Hammam Alshazly & Hany S. Hussein & Thomas Martinetz, 2022. "Trading Stocks Based on Financial News Using Attention Mechanism," Mathematics, MDPI, vol. 10(12), pages 1-30, June.
  17. Ciprian-Octavian Truică & Elena-Simona Apostol, 2023. "It’s All in the Embedding! Fake News Detection Using Document Embeddings," Mathematics, MDPI, vol. 11(3), pages 1-29, January.
  18. 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.
  19. Rajapaksha, Dilini & Bergmeir, Christoph & Hyndman, Rob J., 2023. "LoMEF: A framework to produce local explanations for global model time series forecasts," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1424-1447.
  20. Ahmed Rakha & Hansi Hettiarachchi & Dina Rady & Mohamed Medhat Gaber & Emad Rakha & Mohammed M. Abdelsamea, 2021. "Predicting the Economic Impact of the COVID-19 Pandemic in the United Kingdom Using Time-Series Mining," Economies, MDPI, vol. 9(4), pages 1-19, September.
  21. 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.
  22. Sule Birim & Ipek Kazancoglu & Sachin Kumar Mangla & Aysun Kahraman & Yigit Kazancoglu, 2024. "The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods," Annals of Operations Research, Springer, vol. 339(1), pages 131-161, August.
  23. Sonnleitner, Benedikt & Stapf, Jelena & Wulff, Kai, 2024. "Benchmarking short term forecasts of regional banknote lodgements and withdrawals," Discussion Papers 39/2024, Deutsche Bundesbank.
  24. Saâdaoui, Foued & Rabbouch, Hana, 2024. "Financial forecasting improvement with LSTM-ARFIMA hybrid models and non-Gaussian distributions," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
  25. Mihail Yanchev, 2022. "Deep Growth-at-Risk Model: Nowcasting the 2020 Pandemic Lockdown Recession in Small Open Economies," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 7, pages 20-41.
  26. Wang, Sen & Sun, Yonghui & Zhang, Wenjie & Chung, C.Y. & Srinivasan, Dipti, 2024. "Very short-term wind power forecasting considering static data: An improved transformer model," Energy, Elsevier, vol. 312(C).
  27. Lin, Fan & Zhang, Yao & Wang, Jianxue, 2023. "Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods," International Journal of Forecasting, Elsevier, vol. 39(1), pages 244-265.
  28. Middya, Asif Iqbal & Roy, Sarbani, 2022. "Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
  29. Tomas Kliestik & Alena Novak Sedlackova & Martin Bugaj & Andrej Novak, 2022. "Stability of profits and earnings management in the transport sector of Visegrad countries," Oeconomia Copernicana, Institute of Economic Research, vol. 13(2), pages 475-509, June.
  30. Vivek Kumar Prasad & Pronaya Bhattacharya & Darshil Maru & Sudeep Tanwar & Ashwin Verma & Arunendra Singh & Amod Kumar Tiwari & Ravi Sharma & Ahmed Alkhayyat & Florin-Emilian Țurcanu & Maria Simona Ra, 2022. "Federated Learning for the Internet-of-Medical-Things: A Survey," Mathematics, MDPI, vol. 11(1), pages 1-47, December.
  31. Chuanjie Xie & Chong Huang & Deqiang Zhang & Wei He, 2021. "BiLSTM-I: A Deep Learning-Based Long Interval Gap-Filling Method for Meteorological Observation Data," IJERPH, MDPI, vol. 18(19), pages 1-12, September.
  32. Tsoumalis, Georgios I. & Bampos, Zafeirios N. & Chatzis, Georgios V. & Biskas, Pandelis N. & Keranidis, Stratos D., 2021. "Minimization of natural gas consumption of domestic boilers with convolutional, long-short term memory neural networks and genetic algorithm," Applied Energy, Elsevier, vol. 299(C).
  33. Rameshwar Garg & Shriya Barpanda & Girish Rao Salanke N S & Ramya S, 2022. "Machine Learning Algorithms for Time Series Analysis and Forecasting," Papers 2211.14387, arXiv.org.
  34. Perera, Maneesha & De Hoog, Julian & Bandara, Kasun & Senanayake, Damith & Halgamuge, Saman, 2024. "Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data," Applied Energy, Elsevier, vol. 361(C).
  35. Abdulkadirov, R. & Lyakhov, P. & Bergerman, M. & Reznikov, D., 2024. "Satellite image recognition using ensemble neural networks and difference gradient positive-negative momentum," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
  36. Pierre Dodin & Jingyi Xiao & Yossiri Adulyasak & Neda Etebari Alamdari & Lea Gauthier & Philippe Grangier & Paul Lemaitre & William L. Hamilton, 2023. "Bombardier Aftermarket Demand Forecast with Machine Learning," Interfaces, INFORMS, vol. 53(6), pages 425-445, November.
  37. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
  38. Alfonso Angel Medina-Santana & Leopoldo Eduardo Cárdenas-Barrón, 2022. "Optimal Design of Hybrid Renewable Energy Systems Considering Weather Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 15(23), pages 1-28, November.
  39. Paolo Libenzio Brignoli & Alessandro Varacca & Cornelis Gardebroek & Paolo Sckokai, 2024. "Machine learning to predict grains futures prices," Agricultural Economics, International Association of Agricultural Economists, vol. 55(3), pages 479-497, May.
  40. Zhu, Qing & Che, Jianhua & Liu, Shan, 2024. "Comparative analysis of profits from Bitcoin and its derivatives using artificial intelligence for hedge," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 654(C).
  41. Jujie Wang & Zhenzhen Zhuang & Liu Feng, 2022. "Intelligent Optimization Based Multi-Factor Deep Learning Stock Selection Model and Quantitative Trading Strategy," Mathematics, MDPI, vol. 10(4), pages 1-19, February.
  42. Tea Šestanović & Josip Arnerić, 2021. "Can Recurrent Neural Networks Predict Inflation in Euro Zone as Good as Professional Forecasters?," Mathematics, MDPI, vol. 9(19), pages 1-13, October.
  43. Yongsheng Shi & Leicheng Wang & Na Liao & Zequan Xu, 2025. "Lithium-Ion Battery Degradation Based on the CNN-Transformer Model," Energies, MDPI, vol. 18(2), pages 1-13, January.
  44. Ziyu Li & Xianqi Zhang, 2024. "A Novel Coupled Model for Monthly Rainfall Prediction Based on ESMD-EWT-SVD-LSTM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(9), pages 3297-3312, July.
  45. Zhang, Hanyu & Zandehshahvar, Reza & Tanneau, Mathieu & Van Hentenryck, Pascal, 2025. "Weather-informed probabilistic forecasting and scenario generation in power systems," Applied Energy, Elsevier, vol. 384(C).
  46. Faizal Hafiz & Jan Broekaert & Davide La Torre & Akshya Swain, 2021. "A Multi-criteria Approach to Evolve Sparse Neural Architectures for Stock Market Forecasting," Papers 2111.08060, arXiv.org.
  47. Yannik Hahn & Tristan Langer & Richard Meyes & Tobias Meisen, 2023. "Time Series Dataset Survey for Forecasting with Deep Learning," Forecasting, MDPI, vol. 5(1), pages 1-21, March.
  48. Myladis R. Cogollo & Gilberto González-Parra & Abraham J. Arenas, 2021. "Modeling and Forecasting Cases of RSV Using Artificial Neural Networks," Mathematics, MDPI, vol. 9(22), pages 1-20, November.
  49. Sprangers, Olivier & Schelter, Sebastian & de Rijke, Maarten, 2023. "Parameter-efficient deep probabilistic forecasting," International Journal of Forecasting, Elsevier, vol. 39(1), pages 332-345.
  50. Lee, Yoonjae & Ha, Byeongmin & Hwangbo, Soonho, 2022. "Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Korea's energy transition policy," Renewable Energy, Elsevier, vol. 200(C), pages 69-87.
  51. Yang, Maolin & Li, Muyi & Li, Guodong, 2025. "On memory-augmented gated recurrent unit network," International Journal of Forecasting, Elsevier, vol. 41(2), pages 844-858.
  52. Jing Liu & Xin-Lei Zhou & Lu-Qi Zhang & Yue-Ping Xu, 2023. "Forecasting Short-term Water Demands with an Ensemble Deep Learning Model for a Water Supply System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 2991-3012, June.
  53. Godahewa, Rakshitha & Bergmeir, Christoph & Webb, Geoffrey I. & Montero-Manso, Pablo, 2023. "An accurate and fully-automated ensemble model for weekly time series forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 641-658.
  54. Martin Magris & Mostafa Shabani & Alexandros Iosifidis, 2022. "Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets," Papers 2203.03613, arXiv.org, revised Jan 2023.
  55. Shahab, Muhammad Luthfi & Susanto, Hadi, 2024. "Neural networks for bifurcation and linear stability analysis of steady states in partial differential equations," Applied Mathematics and Computation, Elsevier, vol. 483(C).
  56. Fang, Lei & He, Bin & Yu, Sheng, 2025. "A modular multi-step forecasting method for offshore wind power clusters," Applied Energy, Elsevier, vol. 380(C).
  57. Birim, Şule Öztürk & Kazancoglu, Ipek & Kumar Mangla, Sachin & Kahraman, Aysun & Kumar, Satish & Kazancoglu, Yigit, 2022. "Detecting fake reviews through topic modelling," Journal of Business Research, Elsevier, vol. 149(C), pages 884-900.
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