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DeepAR: Probabilistic forecasting with autoregressive recurrent networks

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Cited by:

  1. Xiangpeng Zhan & Xiaorui Qian & Wei Liu & Xinru Liu & Yuying Chen & Liang Zhang & Huawei Hong & Yimin Shen & Kai Xiao, 2024. "Predicting Industrial Electricity Consumption Using Industry–Geography Relationships: A Graph-Based Machine Learning Approach," Energies, MDPI, vol. 17(17), pages 1-16, August.
  2. 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.
  3. Liu, Xiao & Hu, Qunpeng & Li, Jinsong & Li, Weimin & Liu, Tong & Xin, Mingjun & Jin, Qun, 2024. "Decoupling representation contrastive learning for carbon emission prediction and analysis based on time series," Applied Energy, Elsevier, vol. 367(C).
  4. Dumas, Jonathan & Wehenkel, Antoine & Lanaspeze, Damien & Cornélusse, Bertrand & Sutera, Antonio, 2022. "A deep generative model for probabilistic energy forecasting in power systems: normalizing flows," Applied Energy, Elsevier, vol. 305(C).
  5. Chiew, Ernest & Choong, Shin Siang, 2022. "A solution for M5 Forecasting - Uncertainty: Hybrid gradient boosting and autoregressive recurrent neural network for quantile estimation," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1442-1447.
  6. Conall Butler & Martin Crane, 2023. "Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods," Mathematics, MDPI, vol. 11(9), pages 1-26, May.
  7. 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.
  8. Alcántara Mata, Antonio & Ruiz Mora, Carlos, 2022. "A Neural Network-Based Distributional Constraint Learning Methodology for Mixed-Integer Stochastic Optimization," DES - Working Papers. Statistics and Econometrics. WS 36072, Universidad Carlos III de Madrid. Departamento de Estadística.
  9. 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.
  10. 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.
  11. Le Hoang Anh & Dang Thanh Vu & Seungmin Oh & Gwang-Hyun Yu & Nguyen Bui Ngoc Han & Hyoung-Gook Kim & Jin-Sul Kim & Jin-Young Kim, 2024. "Partial Transfer Learning from Patch Transformer to Variate-Based Linear Forecasting Model," Energies, MDPI, vol. 17(24), pages 1-18, December.
  12. Olivares, Kin G. & Challu, Cristian & Marcjasz, Grzegorz & Weron, Rafał & Dubrawski, Artur, 2023. "Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx," International Journal of Forecasting, Elsevier, vol. 39(2), pages 884-900.
  13. Fan, Jingmin & Zhong, Mingwei & Guan, Yuanpeng & Yi, Siqi & Xu, Cancheng & Zhai, Yanpeng & Zhou, Yongwang, 2024. "An online long-term load forecasting method: Hierarchical highway network based on crisscross feature collaboration," Energy, Elsevier, vol. 299(C).
  14. Shi, Yong & Zhang, Linzi, 2023. "Modelling long- and short-term multi-dimensional patterns in predictive maintenance with accumulative attention," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
  15. Jayesh Thaker & Robert Höller, 2022. "A Comparative Study of Time Series Forecasting of Solar Energy Based on Irradiance Classification," Energies, MDPI, vol. 15(8), pages 1-26, April.
  16. de Rezende, Rafael & Egert, Katharina & Marin, Ignacio & Thompson, Guilherme, 2022. "A white-boxed ISSM approach to estimate uncertainty distributions of Walmart sales," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1460-1467.
  17. Lunacek, Monte & Williams, Lindy & Severino, Joseph & Ficenec, Karen & Ugirumurera, Juliette & Eash, Matthew & Ge, Yanbo & Phillips, Caleb, 2021. "A data-driven operational model for traffic at the Dallas Fort Worth International Airport," Journal of Air Transport Management, Elsevier, vol. 94(C).
  18. Liu, Chen & Wang, Chao & Tran, Minh-Ngoc & Kohn, Robert, 2025. "A long short-term memory enhanced realized conditional heteroskedasticity model," Economic Modelling, Elsevier, vol. 142(C).
  19. Kandaswamy Paramasivan & Brinda Subramani & Nandan Sudarsanam, 2022. "Counterfactual analysis of the impact of the first two waves of the COVID-19 pandemic on the reporting and registration of missing people in India," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-14, December.
  20. Felix Wick & Ulrich Kerzel & Martin Hahn & Moritz Wolf & Trapti Singhal & Daniel Stemmer & Jakob Ernst & Michael Feindt, 2021. "Demand Forecasting of Individual Probability Density Functions with Machine Learning," SN Operations Research Forum, Springer, vol. 2(3), pages 1-39, September.
  21. Jozef Barunik & Lubos Hanus, 2023. "Learning Probability Distributions of Day-Ahead Electricity Prices," Papers 2310.02867, arXiv.org, revised Oct 2023.
  22. 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.
  23. Yin, Linfei & Xiong, Yi, 2024. "Fast-apply deep autoregressive recurrent proximal policy optimization for controlling hot water systems," Applied Energy, Elsevier, vol. 367(C).
  24. Zhong, Mingwei & Fan, Jingmin & Luo, Jianqiang & Xiao, Xuanyi & He, Guanglin & Cai, Rui, 2024. "InfoCAVB-MemoryFormer: Forecasting of wind and photovoltaic power through the interaction of data reconstruction and data augmentation," Applied Energy, Elsevier, vol. 371(C).
  25. Lei Li & Zhiyuan Zhang & Ruihan Bao & Keiko Harimoto & Xu Sun, 2022. "Distributional Correlation--Aware Knowledge Distillation for Stock Trading Volume Prediction," Papers 2208.07232, arXiv.org.
  26. Wang, Xinyu & Ma, Wenping, 2024. "A hybrid deep learning model with an optimal strategy based on improved VMD and transformer for short-term photovoltaic power forecasting," Energy, Elsevier, vol. 295(C).
  27. Yuming Deng & Xinhui Zhang & Tong Wang & Lin Wang & Yidong Zhang & Xiaoqing Wang & Su Zhao & Yunwei Qi & Guangyao Yang & Xuezheng Peng, 2023. "Alibaba Realizes Millions in Cost Savings Through Integrated Demand Forecasting, Inventory Management, Price Optimization, and Product Recommendations," Interfaces, INFORMS, vol. 53(1), pages 32-46, January.
  28. Tuominen, Jalmari & Pulkkinen, Eetu & Peltonen, Jaakko & Kanniainen, Juho & Oksala, Niku & Palomäki, Ari & Roine, Antti, 2024. "Forecasting emergency department occupancy with advanced machine learning models and multivariable input," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1410-1420.
  29. Feifei Huang & Mingxia Lin & Shoukat Iqbal Khattak, 2024. "Form Uncertainty to Sustainable Decision-Making: A Novel MIDAS–AM–DeepAR-Based Prediction Model for E-Commerce Industry Development," Sustainability, MDPI, vol. 16(14), pages 1-24, July.
  30. Ziel, Florian, 2022. "M5 competition uncertainty: Overdispersion, distributional forecasting, GAMLSS, and beyond," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1546-1554.
  31. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhou, Qingyu & Fan, Hang, 2023. "Series-wise attention network for wind power forecasting considering temporal lag of numerical weather prediction," Applied Energy, Elsevier, vol. 336(C).
  32. Ricardo Caetano & José Manuel Oliveira & Patrícia Ramos, 2025. "Transformer-Based Models for Probabilistic Time Series Forecasting with Explanatory Variables," Mathematics, MDPI, vol. 13(5), pages 1-29, February.
  33. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
  34. Sonnleitner, Benedikt & Stapf, Jelena & Wulff, Kai, 2024. "Benchmarking short term forecasts of regional banknote lodgements and withdrawals," Discussion Papers 39/2024, Deutsche Bundesbank.
  35. Tiantian Tu, 2025. "Bridging Short- and Long-Term Dependencies: A CNN-Transformer Hybrid for Financial Time Series Forecasting," Papers 2504.19309, arXiv.org.
  36. Xiong, Binyu & Chen, Yuntian & Chen, Dali & Fu, Jun & Zhang, Dongxiao, 2025. "Deep probabilistic solar power forecasting with Transformer and Gaussian process approximation," Applied Energy, Elsevier, vol. 382(C).
  37. Andreas Lenk & Marcus Vogt & Christoph Herrmann, 2024. "An Approach to Predicting Energy Demand Within Automobile Production Using the Temporal Fusion Transformer Model," Energies, MDPI, vol. 18(1), pages 1-34, December.
  38. Zhou, Zhen & Gu, Ziyuan & Qu, Xiaobo & Liu, Pan & Liu, Zhiyuan & Yu, Wenwu, 2024. "Urban mobility foundation model: A literature review and hierarchical perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 192(C).
  39. Víctor Hugo de la Cruz Madrigal & Liliana Avelar Sosa & Jose-Manuel Mejía-Muñoz & Jorge Luis García Alcaraz & Emilio Jiménez Macías, 2025. "Dynamical System Modeling for Disruption in Supply Chain and Its Detection Using a Data-Driven Deep Learning-Based Architecture," Logistics, MDPI, vol. 9(2), pages 1-23, April.
  40. Wenhui Zhao & Tong Li & Danyang Xu & Zhaohua Wang, 2024. "A global forecasting method of heterogeneous household short-term load based on pre-trained autoencoder and deep-LSTM model," Annals of Operations Research, Springer, vol. 339(1), pages 227-259, August.
  41. Xian, Sidong & Feng, Miaomiao & Cheng, Yue, 2023. "Incremental nonlinear trend fuzzy granulation for carbon trading time series forecast," Applied Energy, Elsevier, vol. 352(C).
  42. Wen, Honglin, 2024. "Probabilistic wind power forecasting resilient to missing values: An adaptive quantile regression approach," Energy, Elsevier, vol. 300(C).
  43. Min Hu & Zhizhong Tan & Bin Liu & Guosheng Yin, 2023. "Futures Quantitative Investment with Heterogeneous Continual Graph Neural Network," Papers 2303.16532, arXiv.org, revised Dec 2023.
  44. Thompson, Ryan & Qian, Yilin & Vasnev, Andrey L., 2024. "Flexible global forecast combinations," Omega, Elsevier, vol. 126(C).
  45. Chen, Yuejiang & Xiao, Jiang-Wen & Wang, Yan-Wu & Luo, Yunfeng, 2025. "Non-crossing quantile probabilistic forecasting of cluster wind power considering spatio-temporal correlation," Applied Energy, Elsevier, vol. 377(PA).
  46. Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023. "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, vol. 125(C).
  47. Heming Chen, 2025. "Can optimal diversification beat the naive 1/N strategy in a highly correlated market? Empirical evidence from cryptocurrencies," Papers 2501.12841, arXiv.org.
  48. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
  49. Anderer, Matthias & Li, Feng, 2022. "Hierarchical forecasting with a top-down alignment of independent-level forecasts," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1405-1414.
  50. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
  51. Dong, Xiaochong & Sun, Yingyun & Dong, Lei & Li, Jian & Li, Yan & Di, Lei, 2023. "Transferable wind power probabilistic forecasting based on multi-domain adversarial networks," Energy, Elsevier, vol. 285(C).
  52. Alberto Mozo & Stanislav Vakaruk & J. Enrique Sierra-García & Antonio Pastor, 2024. "Anticipatory analysis of AGV trajectory in a 5G network using machine learning," Journal of Intelligent Manufacturing, Springer, vol. 35(4), pages 1541-1569, April.
  53. Eikeland, Odin Foldvik & Kelsall, Colin C. & Buznitsky, Kyle & Verma, Shomik & Bianchi, Filippo Maria & Chiesa, Matteo & Henry, Asegun, 2023. "Power availability of PV plus thermal batteries in real-world electric power grids," Applied Energy, Elsevier, vol. 348(C).
  54. Bojer, Casper Solheim, 2022. "Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1555-1561.
  55. Jože Martin Rožanec & Blaž Fortuna & Dunja Mladenić, 2022. "Reframing Demand Forecasting: A Two-Fold Approach for Lumpy and Intermittent Demand," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
  56. Davood Pirayesh Neghab & Mucahit Cevik & M. I. M. Wahab & Ayse Basar, 2025. "Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning," Computational Economics, Springer;Society for Computational Economics, vol. 65(4), pages 1857-1899, April.
  57. Bojer, Casper Solheim & Meldgaard, Jens Peder, 2021. "Kaggle forecasting competitions: An overlooked learning opportunity," International Journal of Forecasting, Elsevier, vol. 37(2), pages 587-603.
  58. Ying Shu & Chengfu Ding & Lingbing Tao & Chentao Hu & Zhixin Tie, 2023. "Air Pollution Prediction Based on Discrete Wavelets and Deep Learning," Sustainability, MDPI, vol. 15(9), pages 1-19, April.
  59. Zhuoyuan Lyu & Ying Shen & Yu Zhao & Tao Hu, 2023. "Solar Radiation Prediction Based on Conformer-GLaplace-SDAR Model," Sustainability, MDPI, vol. 15(20), pages 1-18, October.
  60. Sonan Memon, 2022. "Inflation in Pakistan: High-Frequency Estimation and Forecasting," PIDE-Working Papers 2022:12, Pakistan Institute of Development Economics.
  61. Xu, Shilin & Liu, Yang & Jin, Chun, 2023. "Forecasting daily tourism demand with multiple factors," Annals of Tourism Research, Elsevier, vol. 103(C).
  62. Shalini Sharma & Angshul Majumdar & Emilie Chouzenoux & Victor Elvira, 2023. "Deep State-Space Model for Predicting Cryptocurrency Price," Papers 2311.14731, arXiv.org.
  63. Shao, Zhen & Yang, Yudie & Zheng, Qingru & Zhou, Kaile & Liu, Chen & Yang, Shanlin, 2022. "A pattern classification methodology for interval forecasts of short-term electricity prices based on hybrid deep neural networks: A comparative analysis," Applied Energy, Elsevier, vol. 327(C).
  64. Niu, Zhewen & Han, Xiaoqing & Zhang, Dongxia & Wu, Yuxiang & Lan, Songyan, 2024. "Interpretable wind power forecasting combining seasonal-trend representations learning with temporal fusion transformers architecture," Energy, Elsevier, vol. 306(C).
  65. Jiawei Zhang & Rongquan Zhang & Yanfeng Zhao & Jing Qiu & Siqi Bu & Yuxiang Zhu & Gangqiang Li, 2023. "Deterministic and Probabilistic Prediction of Wind Power Based on a Hybrid Intelligent Model," Energies, MDPI, vol. 16(10), pages 1-15, May.
  66. Sergio Consoli & Luca Tiozzo Pezzoli & Elisa Tosetti, 2022. "Neural forecasting of the Italian sovereign bond market with economic news," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 197-224, December.
  67. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org, revised Oct 2024.
  68. 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.
  69. Heejong Lim & Kwanghun Chung & Sangbok Lee, 2022. "Probabilistic Forecasting for Demand of a Bike-Sharing Service Using a Deep-Learning Approach," Sustainability, MDPI, vol. 14(23), pages 1-18, November.
  70. Jan Groeneveld & Judith Herrmann & Nikkel Mollenhauer & Leonard Dreeßen & Nick Bessin & Johann Schulze Tast & Alexander Kastius & Johannes Huegle & Rainer Schlosser, 2024. "Self-learning Agents for Recommerce Markets," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 66(4), pages 441-463, August.
  71. Lucas Mussoi Almeida & Fernanda Maria Müller & Marcelo Scherer Perlin, 2025. "Risk Forecasting Comparisons in Decentralized Finance: An Approach in Constant Product Market Makers," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 395-428, January.
  72. 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.
  73. Wang, Shengjie & Kang, Yanfei & Petropoulos, Fotios, 2024. "Combining probabilistic forecasts of intermittent demand," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1038-1048.
  74. Qian, Yilin & Thompson, Ryan & Vasnev, Andrey L, 2022. "Global combinations of expert forecasts," Working Papers BAWP-2022-02, University of Sydney Business School, Discipline of Business Analytics.
  75. Lars Ødegaard Bentsen & Narada Dilp Warakagoda & Roy Stenbro & Paal Engelstad, 2023. "A Unified Graph Formulation for Spatio-Temporal Wind Forecasting," Energies, MDPI, vol. 16(20), pages 1-23, October.
  76. Wenhao Guo & Yuda Wang & Zeqiao Huang & Changjiang Zhang & Shumin ma, 2025. "Trading Under Uncertainty: A Distribution-Based Strategy for Futures Markets Using FutureQuant Transformer," Papers 2505.05595, arXiv.org.
  77. Xiaohang Ren & Wenting Jiang & Qiang Ji & Pengxiang Zhai, 2024. "Seeing is believing: Forecasting crude oil price trend from the perspective of images," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2809-2821, November.
  78. Pesantez, Jorge E. & Li, Binbin & Lee, Christopher & Zhao, Zhizhen & Butala, Mark & Stillwell, Ashlynn S., 2023. "A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment," Energy, Elsevier, vol. 283(C).
  79. Wen, Honglin & Pinson, Pierre & Gu, Jie & Jin, Zhijian, 2024. "Wind energy forecasting with missing values within a fully conditional specification framework," International Journal of Forecasting, Elsevier, vol. 40(1), pages 77-95.
  80. Haodong Wang & Huaxiong Zhang, 2025. "An Anomaly Detection Method for Multivariate Time Series Data Based on Variational Autoencoders and Association Discrepancy," Mathematics, MDPI, vol. 13(7), pages 1-17, April.
  81. Jozef Barunik & Lubos Hanus, 2022. "Learning Probability Distributions in Macroeconomics and Finance," Papers 2204.06848, arXiv.org.
  82. Kandaswamy Paramasivan & Rahul Subburaj & Saish Jaiswal & Nandan Sudarsanam, 2022. "Empirical evidence of the impact of mobility on property crimes during the first two waves of the COVID-19 pandemic," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-14, December.
  83. Jozef Baruník & Luboš Hanus, 2025. "Taming Data‐Driven Probability Distributions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 676-691, March.
  84. Redouane Benabdallah Benarmas & Kadda Beghdad Bey, 2024. "A deep learning hierarchical approach to road traffic forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1294-1307, August.
  85. Seungmin Oh & Le Hoang Anh & Dang Thanh Vu & Gwang Hyun Yu & Minsoo Hahn & Jinsul Kim, 2024. "Patch-Wise-Based Self-Supervised Learning for Anomaly Detection on Multivariate Time Series Data," Mathematics, MDPI, vol. 12(24), pages 1-17, December.
  86. Chen, Yuejiang & He, Yingjing & Xiao, Jiang-Wen & Wang, Yan-Wu & Li, Yuanzheng, 2024. "Hybrid model based on similar power extraction and improved temporal convolutional network for probabilistic wind power forecasting," Energy, Elsevier, vol. 304(C).
  87. Liu, Lei & Wang, Xinyu & Dong, Xue & Chen, Kang & Chen, Qiuju & Li, Bin, 2024. "Interpretable feature-temporal transformer for short-term wind power forecasting with multivariate time series," Applied Energy, Elsevier, vol. 374(C).
  88. 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.
  89. Zexing Xu & Linjun Zhang & Sitan Yang & Rasoul Etesami & Hanghang Tong & Huan Zhang & Jiawei Han, 2024. "F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data," Papers 2406.16221, arXiv.org.
  90. Park, Jungyeon & Alvarenga, Estêvão & Jeon, Jooyoung & Li, Ran & Petropoulos, Fotios & Kim, Hokyun & Ahn, Kwangwon, 2024. "Probabilistic forecast-based portfolio optimization of electricity demand at low aggregation levels," Applied Energy, Elsevier, vol. 353(PB).
  91. Baruník, Jozef & Hanus, Luboš, 2024. "Fan charts in era of big data and learning," Finance Research Letters, Elsevier, vol. 61(C).
  92. Elham M. Al-Ali & Yassine Hajji & Yahia Said & Manel Hleili & Amal M. Alanzi & Ali H. Laatar & Mohamed Atri, 2023. "Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model," Mathematics, MDPI, vol. 11(3), pages 1-19, January.
  93. Zhang, Chao & Ma, Yunfeng & Mi, Zengqiang & Yang, Fan & Zhang, Long, 2024. "A rolling-horizon cleaning recommendation system for dust removal of industrial PV panels," Applied Energy, Elsevier, vol. 353(PB).
  94. Zhou, Heng & Zheng, Peijun & Dong, Jiuqing & Liu, Jiang & Nakanishi, Yosuke, 2024. "Interpretable feature selection and deep learning for short-term probabilistic PV power forecasting in buildings using local monitoring data," Applied Energy, Elsevier, vol. 376(PA).
  95. 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.
  96. 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.
  97. Jakub Horak & Tomas Krulicky & Zuzana Rowland & Veronika Machova, 2020. "Creating a Comprehensive Method for the Evaluation of a Company," Sustainability, MDPI, vol. 12(21), pages 1-23, November.
  98. Yuanhong Mao & Xin Hu & Yulang Xu & Yilin Zhang & Yunan Li & Zixiang Lu & Qiguang Miao, 2025. "Decomposition-Aware Framework for Probabilistic and Flexible Time Series Forecasting in Aerospace Electronic Systems," Mathematics, MDPI, vol. 13(2), pages 1-23, January.
  99. Zheng, Peijun & Zhou, Heng & Liu, Jiang & Nakanishi, Yosuke, 2023. "Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture," Applied Energy, Elsevier, vol. 349(C).
  100. Laeeq Aslam & Runmin Zou & Ebrahim Shahzad Awan & Sayyed Shahid Hussain & Kashish Ara Shakil & Mudasir Ahmad Wani & Muhammad Asim, 2025. "Hardware-Centric Exploration of the Discrete Design Space in Transformer–LSTM Models for Wind Speed Prediction on Memory-Constrained Devices," Energies, MDPI, vol. 18(9), pages 1-21, April.
  101. Lin, Jiahe & Michailidis, George, 2024. "A multi-task encoder-dual-decoder framework for mixed frequency data prediction," International Journal of Forecasting, Elsevier, vol. 40(3), pages 942-957.
  102. Evangelos Spiliotis & Spyros Makridakis & Artemios-Anargyros Semenoglou & Vassilios Assimakopoulos, 2022. "Comparison of statistical and machine learning methods for daily SKU demand forecasting," Operational Research, Springer, vol. 22(3), pages 3037-3061, July.
  103. Jiang, Zongxi & Zhang, Luliang & Ji, Tianyao, 2023. "NSDAR: A neural network-based model for similar day screening and electric load forecasting," Applied Energy, Elsevier, vol. 349(C).
  104. Long, Xueying & Bui, Quang & Oktavian, Grady & Schmidt, Daniel F. & Bergmeir, Christoph & Godahewa, Rakshitha & Lee, Seong Per & Zhao, Kaifeng & Condylis, Paul, 2025. "Scalable probabilistic forecasting in retail with gradient boosted trees: A practitioner’s approach," International Journal of Production Economics, Elsevier, vol. 279(C).
  105. Dinggao Liu & Liuqing Wang & Shuo Lin & Zhenpeng Tang, 2025. "A Novel Multi-Task Learning Framework for Interval-Valued Carbon Price Forecasting Using Online News and Search Engine Data," Mathematics, MDPI, vol. 13(3), pages 1-23, January.
  106. Grzegorz Dudek, 2021. "Short-Term Load Forecasting Using Neural Networks with Pattern Similarity-Based Error Weights," Energies, MDPI, vol. 14(11), pages 1-18, May.
  107. Deng, Jiewen & Xiao, Zhao & Zhao, Qiancheng & Zhan, Jun & Tao, Jie & Liu, Minghua & Song, Dongran, 2024. "Wind turbine short-term power forecasting method based on hybrid probabilistic neural network," Energy, Elsevier, vol. 313(C).
  108. Zhang, Luliang & Jiang, Zongxi & Ji, Tianyao & Chen, Ziming, 2025. "Diffusion-based inpainting approach for multifunctional short-term load forecasting," Applied Energy, Elsevier, vol. 377(PB).
  109. Zhang, Rongquan & Bu, Siqi & Li, Gangqiang, 2024. "Multi-market P2P trading of cooling–heating-power-hydrogen integrated energy systems: An equilibrium-heuristic online prediction optimization approach," Applied Energy, Elsevier, vol. 367(C).
  110. Zhao, Lingxiao & Li, Zhiyang & Pei, Yuguo & Qu, Leilei, 2024. "Disentangled Seasonal-Trend representation of improved CEEMD-GRU joint model with entropy-driven reconstruction to forecast significant wave height," Renewable Energy, Elsevier, vol. 226(C).
  111. Frison, Lilli & Gölzhäuser, Simon & Bitterling, Moritz & Kramer, Wolfgang, 2024. "Evaluating different artificial neural network forecasting approaches for optimizing district heating network operation," Energy, Elsevier, vol. 307(C).
  112. Sergiy Tkachuk & Szymon {L}ukasik & Anna Wr'oblewska, 2024. "Consumer Transactions Simulation through Generative Adversarial Networks," Papers 2408.03655, arXiv.org.
  113. Agakishiev, Ilyas & Härdle, Wolfgang Karl & Kopa, Milos & Kozmik, Karel & Petukhina, Alla, 2025. "Multivariate probabilistic forecasting of electricity prices with trading applications," Energy Economics, Elsevier, vol. 141(C).
  114. Georgios Fatouros & Georgios Makridis & Dimitrios Kotios & John Soldatos & Michael Filippakis & Dimosthenis Kyriazis, 2023. "DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks," Digital Finance, Springer, vol. 5(1), pages 29-56, March.
  115. Pengfei Zhao & Haoren Zhu & Wilfred Siu Hung NG & Dik Lun Lee, 2024. "From GARCH to Neural Network for Volatility Forecast," Papers 2402.06642, arXiv.org.
  116. Xu, Mengjie & Li, Qianwen & Zhao, Zhengtang & Sun, Chuanwang, 2024. "Bilinear-DRTFT: Uncertainty prediction in electricity load considering multiple demand responses," Energy, Elsevier, vol. 309(C).
  117. Baggio, Roberta & Muzy, Jean-François, 2024. "Improving probabilistic wind speed forecasting using M-Rice distribution and spatial data integration," Applied Energy, Elsevier, vol. 360(C).
  118. Li, Xixi & Yuan, Jingsong, 2024. "DeepTVAR: Deep learning for a time-varying VAR model with extension to integrated VAR," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1123-1133.
  119. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "M5 accuracy competition: Results, findings, and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1346-1364.
  120. Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2023. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Papers 2401.00249, arXiv.org, revised Jul 2024.
  121. 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.
  122. Yu, Chuanjin & Fu, Suxiang & Wei, ZiWei & Zhang, Xiaochi & Li, Yongle, 2024. "Multi-feature-fused generative neural network with Gaussian mixture for multi-step probabilistic wind speed prediction," Applied Energy, Elsevier, vol. 359(C).
  123. Zhen Zeng & Rachneet Kaur & Suchetha Siddagangappa & Saba Rahimi & Tucker Balch & Manuela Veloso, 2023. "Financial Time Series Forecasting using CNN and Transformer," Papers 2304.04912, arXiv.org.
  124. Daniel Hopp, 2024. "Benchmarking econometric and machine learning methodologies in nowcasting GDP," Empirical Economics, Springer, vol. 66(5), pages 2191-2247, May.
  125. Paolo Maranzano & Paul A. Parker, 2025. "Discussion on “Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models”," Environmetrics, John Wiley & Sons, Ltd., vol. 36(2), March.
  126. 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.
  127. Junfeng Yu & Xiaodong Li & Lei Yang & Linze Li & Zhichao Huang & Keyan Shen & Xu Yang & Xu Yang & Zhikang Xu & Dongying Zhang & Shuai Du, 2024. "Deep Learning Models for PV Power Forecasting: Review," Energies, MDPI, vol. 17(16), pages 1-35, August.
  128. Kevin Xin & Lizhi Xin, 2024. "QxEAI: Quantum-like evolutionary algorithm for automated probabilistic forecasting," Papers 2405.03701, arXiv.org, revised Jun 2024.
  129. Joaquin Gonzalez & Liliana Avelar Sosa & Gabriel Bravo & Oliverio Cruz-Mejia & Jose-Manuel Mejia-Muñoz, 2024. "Fog Computing and Industry 4.0 for Newsvendor Inventory Model Using Attention Mechanism and Gated Recurrent Unit," Logistics, MDPI, vol. 8(2), pages 1-14, June.
  130. Bartłomiej Gaweł & Andrzej Paliński, 2024. "Global and Local Approaches for Forecasting of Long-Term Natural Gas Consumption in Poland Based on Hierarchical Short Time Series," Energies, MDPI, vol. 17(2), pages 1-25, January.
  131. Hao Xu & Jinglong Lin & Dongxiao Zhang & Fanyang Mo, 2023. "Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  132. Chaokai Huang & Ning Du & Jiahan He & Na Li & Yifan Feng & Weihong Cai, 2023. "Multidimensional Feature-Based Graph Attention Networks and Dynamic Learning for Electricity Load Forecasting," Energies, MDPI, vol. 16(18), pages 1-17, September.
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