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DeepAR: Probabilistic forecasting with autoregressive recurrent networks

Citations

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Cited by:

  1. Baruník, Jozef & Hanus, Luboš, 2024. "Fan charts in era of big data and learning," Finance Research Letters, Elsevier, vol. 61(C).
  2. Ozan Ozyegen & Garima Malik & Mucahit Cevik & Kevin Ioi & Karim El Mokhtari, 2026. "A unified framework for financial commentary prediction," Information Technology and Management, Springer, vol. 27(1), pages 95-111, March.
  3. 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.
  4. Hauzenberger, Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2025. "Bayesian neural networks for macroeconomic analysis," Journal of Econometrics, Elsevier, vol. 249(PC).
  5. 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).
  6. 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).
  7. 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.
  8. 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.
  9. 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.
  10. Liu, Jingxuan & Zang, Haixiang & Cheng, Lilin & Ding, Tao & Wei, Zhinong & Sun, Guoqiang, 2025. "Generative probabilistic forecasting of wind power: A Denoising-Diffusion-based nonstationary signal modeling approach," Energy, Elsevier, vol. 317(C).
  11. 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).
  12. 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).
  13. Ioannis Papageorgiou & Ioannis Kontoyiannis, 2023. "The Bayesian Context Trees State Space Model for time series modelling and forecasting," Papers 2308.00913, arXiv.org, revised Aug 2025.
  14. Oktay Sahinoglu & Ayca Kumluca Topalli & Ihsan Topalli, 2025. "Discovering Granger causality with convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 36(8), pages 5967-5980, December.
  15. 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.
  16. Jaemoo Hong & Yoon Min Hwang, 2025. "Long short-term memory networks in learning memory inconsistencies of stock markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-50, December.
  17. 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.
  18. 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.
  19. 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).
  20. 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.
  21. 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.
  22. Xian, Sidong & Feng, Miaomiao & Cheng, Yue, 2023. "Incremental nonlinear trend fuzzy granulation for carbon trading time series forecast," Applied Energy, Elsevier, vol. 352(C).
  23. 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).
  24. Chen Chuanglian & Lin Huanheng & Lin Yuting, 2026. "Deep Learning‐Based Network Relationship Construction Method and Its Impact on Futures Risk Premiums," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 46(1), pages 175-196, January.
  25. 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.
  26. Wen, Honglin, 2024. "Probabilistic wind power forecasting resilient to missing values: An adaptive quantile regression approach," Energy, Elsevier, vol. 300(C).
  27. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Papers 2311.16333, arXiv.org, revised Apr 2024.
  28. 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.
  29. 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.
  30. 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.
  31. 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.
  32. 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.
  33. Conall Butler & Martin Crane, 2023. "Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods," Mathematics, MDPI, vol. 11(9), pages 1-26, May.
  34. 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.
  35. Thompson, Ryan & Qian, Yilin & Vasnev, Andrey L., 2024. "Flexible global forecast combinations," Omega, Elsevier, vol. 126(C).
  36. Zijiang Yang & Tad Gonsalves, 2025. "SegmentedCrossformer—A Novel and Enhanced Cross-Time and Cross-Dimensional Transformer for Multivariate Time Series Forecasting," Forecasting, MDPI, vol. 7(3), pages 1-20, August.
  37. 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).
  38. 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).
  39. 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.
  40. 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).
  41. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org, revised Oct 2024.
  42. 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.
  43. 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.
  44. 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.
  45. Liang, Qian & Lin, Qingyuan & Guo, Mengzhuo & Lu, Quanying & Zhang, Dayong, 2025. "Forecasting crude oil prices: A Gated Recurrent Unit-based nonlinear Granger Causality model," International Review of Financial Analysis, Elsevier, vol. 102(C).
  46. Theodoros Zafeiriou & Dimitris Kalles, 2024. "Off-the-Shelf Neural Network Architectures for Forex Time Series Prediction come at a Cost," Papers 2405.10679, arXiv.org.
  47. 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.
  48. 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.
  49. 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).
  50. Samartzis, Panagiotis, 2025. "Predicting the relative performance among financial assets: A comparative analysis of different approaches," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1428-1449.
  51. 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.
  52. Harsha Chamara Hewage & H. Niles Perera & Kasun Bandara, 2026. "Enhancing Demand Forecasting in Retail: A Comprehensive Analysis of Sales Promotional Effects on the Entire Demand Life Cycle," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(1), pages 293-315, January.
  53. 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.
  54. 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.
  55. Ai, Hongfeng & Liu, Chenning & Lin, Peng, 2025. "Robust returns ranking prediction and portfolio optimization for M6," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1494-1504.
  56. Jinho Cha & Sahng-Min Han & Long Pham, 2025. "Smart Contract Adoption under Discrete Overdispersed Demand: A Negative Binomial Optimization Perspective," Papers 2510.05487, arXiv.org.
  57. Lilin Fan & Zhaoyu Song & Wentao Mao & Tiejun Luo & Wanting Wang & Kai Yang & Fukang Cao, 2025. "Change is safer: a dynamic safety stock model for inventory management of large manufacturing enterprise based on intermittent time series forecasting," Journal of Intelligent Manufacturing, Springer, vol. 36(6), pages 3983-4003, August.
  58. 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.
  59. 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.
  60. Heming Chen & Xiaojing Cai, 2025. "Optimal vs. Naive Diversification in the Cryptocurrencies Market: The Role of Time-Varying Moments and Transaction Costs," Papers 2501.12841, arXiv.org, revised Nov 2025.
  61. 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).
  62. Xia, Chenyue & Xu, Yinliang & Tai, Nengling & Sun, Hongbin, 2026. "A dual-layer feature-selection transformer network for transferable probabilistic forecasting of PV power," Applied Energy, Elsevier, vol. 406(C).
  63. 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.
  64. Feddersen, Leif & Cleophas, Catherine, 2026. "Hierarchical neural additive models for interpretable demand forecasts," International Journal of Forecasting, Elsevier, vol. 42(1), pages 216-234.
  65. Daozheng Qu & Yanfei Ma, 2025. "MaGNet-BN: Markov-Guided Bayesian Neural Networks for Calibrated Long-Horizon Sequence Forecasting and Community Tracking," Mathematics, MDPI, vol. 13(17), pages 1-28, August.
  66. Keyan Jin & Francisco Javier Blanco‐Encomienda, 2026. "Seasonal Decomposition‐Enhanced Deep Learning Architecture for Probabilistic Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(2), pages 880-891, March.
  67. 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).
  68. Deng, Ruizhe & Wang, Yiming & Xu, Po & Luo, Futao & Chen, Qi & Zhang, Haoran & Chen, Yuntian & Zhang, Dongxiao, 2025. "A high-precision photovoltaic power forecasting model leveraging low-fidelity data through decoupled informer with multi-moment guidance," Renewable Energy, Elsevier, vol. 250(C).
  69. 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).
  70. Patrik Andersson & Mathias Lindholm, 2026. "Mortality Forecasting Using Variational Inference," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(3), pages 1069-1076, April.
  71. Jiang, Shang & Tran, Cong Quoc & Keyvan-Ekbatani, Mehdi, 2025. "A diffusion-model-based approach for forecasting energy demand in New Zealand’s transport sector," Applied Energy, Elsevier, vol. 400(C).
  72. Yuan, Shuang & Jia, Peng & Liu, Qing & Si, Ruibin, 2025. "Unraveling the dynamics of China railway express (CRE) in China: A multi-method analysis," Transport Policy, Elsevier, vol. 171(C), pages 370-388.
  73. Seongjin Choi & Nicolas Saunier & Vincent Zhihao Zheng & Martin Trépanier & Lijun Sun, 2025. "Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting," Transportation Science, INFORMS, vol. 59(4), pages 708-720, July.
  74. Philippe Goulet Coulombe, 2026. "Quantifying the Risk-Return Tradeoff in Forecasting," Papers 2605.09712, arXiv.org.
  75. 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.
  76. 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.
  77. Hu, Lipeng & Tang, Jinjun & Xu, Fuqiao & Liang, Xiao, 2025. "SOC prediction for electric buses based on interpretable transformer model: Impact of traffic conditions and feature importance," Energy, Elsevier, vol. 324(C).
  78. 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.
  79. 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.
  80. 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).
  81. 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.
  82. 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).
  83. 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).
  84. Xu, Yongzhuo & Kang, Bingyi, 2025. "A novel model based on graph kernel and S-R score in visibility graph for time series forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 674(C).
  85. 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).
  86. Jakub Micha'nk'ow, 2025. "Forecasting Probability Distributions of Financial Returns with Deep Neural Networks," Papers 2508.18921, arXiv.org, revised Aug 2025.
  87. Lakshmi Devi Pujari & Sridhar C. Naga Venkata & Saayee Saahit CNV & Swetha Reddy Ravula, 2026. "Predictive and Prescriptive Logistics Optimization Using Hybrid AI, Time-Series Analytics, and Synthetic Data: A Case Study," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 10(14), pages 68-77, January.
  88. Oliver Stover & Pranav Karve & Sankaran Mahadevan, 2026. "Periodic Regression in the Principal Component Space for Multivariate, Multi‐Horizon, Probabilistic Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(3), pages 1292-1310, April.
  89. Sergiy Tkachuk & Szymon {L}ukasik & Anna Wr'oblewska, 2024. "Consumer Transactions Simulation through Generative Adversarial Networks," Papers 2408.03655, arXiv.org.
  90. 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).
  91. 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.
  92. 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.
  93. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2026. "From Reactive to Proactive Volatility Modeling With Hemisphere Neural Networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 41(3), pages 265-279, April.
  94. Bu, Zhiyuan & Long, Bing & Liu, Zhen & Wu, Kunping & Geng, Hang & Cheng, Yuhua, 2025. "Multivariate adaptive Brownian Motion-Particle Filter framework for remaining useful life prediction of nonlinear and state-noise coupled degradation process," Reliability Engineering and System Safety, Elsevier, vol. 264(PB).
  95. 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).
  96. 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.
  97. Mansur M. Arief, 2026. "Deepbullwhip: An Open-Source Simulation and Benchmarking for Multi-Echelon Bullwhip Analyses," Papers 2604.13478, arXiv.org.
  98. Zeng, Huanze & Shi, Chenlu & Fang, Haoyu & Wu, Binrong, 2025. "Interpretable multivariate wind speed forecasting using sliding masked window-based decomposition and deep autoregressive networks," Energy, Elsevier, vol. 341(C).
  99. Yinuo Sun & Zhaoen Qu & Zhuodong Liu & Xiangyu Li, 2025. "Hierarchical Multi-Scale Decomposition and Deep Learning Ensemble Framework for Enhanced Carbon Emission Prediction," Mathematics, MDPI, vol. 13(12), pages 1-34, June.
  100. Chen, Xiaoxu & Cheng, Zhanhong & Schmidt, Alexandra M. & Sun, Lijun, 2025. "Conditional forecasting of bus travel time and passenger occupancy with Bayesian Markov regime-switching vector autoregression," Transportation Research Part B: Methodological, Elsevier, vol. 192(C).
  101. 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).
  102. 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).
  103. 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).
  104. 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.
  105. 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.
  106. 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).
  107. Hanus, Luboš & Baruník, Jozef, 2025. "Learning the probability distributions of day-ahead electricity prices," Energy Economics, Elsevier, vol. 152(C).
  108. 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," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 9(1), pages 1-14, December.
  109. 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.
  110. 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).
  111. 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.
  112. 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.
  113. Pradeep Singh & Balasubramanian Raman, 2025. "Turning Time Into Shapes: A Point‐Cloud Framework With Chaotic Signatures for Time Series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(7), pages 2089-2105, November.
  114. Catlin, Colin, 2025. "Adaptive forecasting in dynamic markets: An evaluation of AutoTS within the M6 competition," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1485-1493.
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