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DeepAR: Probabilistic forecasting with autoregressive recurrent networks

Citations

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Cited by:

  1. 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.
  2. Hauzenberger, Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2025. "Bayesian neural networks for macroeconomic analysis," Journal of Econometrics, Elsevier, vol. 249(PC).
  3. 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.
  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. 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).
  6. 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.
  7. 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).
  8. 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.
  9. Conall Butler & Martin Crane, 2023. "Blockchain Transaction Fee Forecasting: A Comparison of Machine Learning Methods," Mathematics, MDPI, vol. 11(9), pages 1-26, May.
  10. 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.
  11. 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.
  12. 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.
  13. 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).
  14. 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.
  15. 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.
  16. 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.
  17. 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).
  18. Feddersen, Leif & Cleophas, Catherine, 2026. "Hierarchical neural additive models for interpretable demand forecasts," International Journal of Forecasting, Elsevier, vol. 42(1), pages 216-234.
  19. 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.
  20. 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).
  21. Patrik Andersson & Mathias Lindholm, 2026. "Mortality Forecasting Using Variational Inference," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(3), pages 1069-1076, April.
  22. 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).
  23. 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.
  24. 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.
  25. 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.
  26. 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).
  27. 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).
  28. 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).
  29. 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).
  30. 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.
  31. 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.
  32. Hanus, Luboš & Baruník, Jozef, 2025. "Learning the probability distributions of day-ahead electricity prices," Energy Economics, Elsevier, vol. 152(C).
  33. 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.
  34. 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.
  35. Yin, Linfei & Xiong, Yi, 2024. "Fast-apply deep autoregressive recurrent proximal policy optimization for controlling hot water systems," Applied Energy, Elsevier, vol. 367(C).
  36. 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).
  37. 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.
  38. 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).
  39. 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.
  40. 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.
  41. 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.
  42. Ziel, Florian, 2022. "M5 competition uncertainty: Overdispersion, distributional forecasting, GAMLSS, and beyond," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1546-1554.
  43. 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).
  44. Xu, Xin & Cao, Qinglong & Deng, Ruizhe & Guo, Zhiling & Chen, Yuntian & Yan, Jinyue, 2025. "A cross-dataset benchmark for neural network-based wind power forecasting," Renewable Energy, Elsevier, vol. 254(C).
  45. 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.
  46. 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).
  47. Sonnleitner, Benedikt & Stapf, Jelena & Wulff, Kai, 2024. "Benchmarking short term forecasts of regional banknote lodgements and withdrawals," Discussion Papers 39/2024, Deutsche Bundesbank.
  48. Tiantian Tu, 2025. "Bridging Short- and Long-Term Dependencies: A CNN-Transformer Hybrid for Financial Time Series Forecasting," Papers 2504.19309, arXiv.org.
  49. 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).
  50. 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.
  51. 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).
  52. 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.
  53. 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.
  54. 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.
  55. Xian, Sidong & Feng, Miaomiao & Cheng, Yue, 2023. "Incremental nonlinear trend fuzzy granulation for carbon trading time series forecast," Applied Energy, Elsevier, vol. 352(C).
  56. Wen, Honglin, 2024. "Probabilistic wind power forecasting resilient to missing values: An adaptive quantile regression approach," Energy, Elsevier, vol. 300(C).
  57. 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.
  58. Thompson, Ryan & Qian, Yilin & Vasnev, Andrey L., 2024. "Flexible global forecast combinations," Omega, Elsevier, vol. 126(C).
  59. 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.
  60. 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).
  61. 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.
  62. 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.
  63. 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.
  64. 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.
  65. 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.
  66. 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.
  67. 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).
  68. 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.
  69. 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).
  70. 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.
  71. 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).
  72. 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).
  73. 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.
  74. 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.
  75. Abdelfatah, Omar Sharafeldin Mohamed, 2026. "AI-Driven Demand Forecasting and Its Impact on Inventory Optimization," SocArXiv uw57j_v1, Center for Open Science.
  76. Meisenbacher, Stefan & Phipps, Kaleb & Taubert, Oskar & Weiel, Marie & Götz, Markus & Mikut, Ralf & Hagenmeyer, Veit, 2025. "AutoPQ: Automating quantile estimation from point forecasts in the context of sustainability," Applied Energy, Elsevier, vol. 392(C).
  77. 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.
  78. 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.
  79. 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.
  80. Carlo Mari & Carlo Lucheroni, 2025. "Hierarchical Vector Mixtures for Electricity Day-Ahead Market Prices Scenario Generation," Mathematics, MDPI, vol. 13(17), pages 1-40, September.
  81. 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.
  82. Ciaran O’Connor & Mohamed Bahloul & Steven Prestwich & Andrea Visentin, 2025. "A Review of Electricity Price Forecasting Models in the Day-Ahead, Intra-Day, and Balancing Markets," Energies, MDPI, vol. 18(12), pages 1-40, June.
  83. Sengupta, Shovon & Chakraborty, Tanujit & Singh, Sunny Kumar, 2025. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," International Journal of Forecasting, Elsevier, vol. 41(3), pages 953-981.
  84. 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.
  85. Sonan Memon, 2022. "Inflation in Pakistan: High-Frequency Estimation and Forecasting," PIDE-Working Papers 2022:12, Pakistan Institute of Development Economics.
  86. Xu, Shilin & Liu, Yang & Jin, Chun, 2023. "Forecasting daily tourism demand with multiple factors," Annals of Tourism Research, Elsevier, vol. 103(C).
  87. Arundeep Chinta & Lucas Vinh Tran & Jay Katukuri, 2026. "ProbFM: Probabilistic Time Series Foundation Model with Uncertainty Decomposition," Papers 2601.10591, arXiv.org.
  88. Shalini Sharma & Angshul Majumdar & Emilie Chouzenoux & Victor Elvira, 2023. "Deep State-Space Model for Predicting Cryptocurrency Price," Papers 2311.14731, arXiv.org.
  89. 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).
  90. 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).
  91. 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.
  92. 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.
  93. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org, revised Oct 2024.
  94. 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.
  95. 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.
  96. 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.
  97. 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.
  98. 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.
  99. 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.
  100. 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.
  101. 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.
  102. 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.
  103. 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.
  104. Jakub Micha'nk'ow, 2025. "Forecasting Probability Distributions of Financial Returns with Deep Neural Networks," Papers 2508.18921, arXiv.org, revised Aug 2025.
  105. 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.
  106. 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.
  107. 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.
  108. 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).
  109. 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.
  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. 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.
  112. 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.
  113. Jozef Barunik & Lubos Hanus, 2022. "Learning Probability Distributions in Macroeconomics and Finance," Papers 2204.06848, arXiv.org.
  114. 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," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 9(1), pages 1-14, December.
  115. Jozef Baruník & Luboš Hanus, 2025. "Taming Data‐Driven Probability Distributions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 676-691, March.
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