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Pretrained time series models have enabled inference-only forecasting systems that produce accurate predictions without task-specific training.
Alternative transformations to handle extreme values of the dependent variable
John B Burbidge, Lonnie Magee, and A Leslie Robb · 1988
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The theta model: a decomposition approach to forecasting
V. Assimakopoulos and K. Nikolopoulos · 2000
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A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems
Daniele Micci-Barreca · 2001
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Detecting strange attractors in turbulence
Floris Takens · 2006
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Large bayesian vector auto regressions
Marta Bańbura, Domenico Giannone, and Lucrezia Reichlin · 2010
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statsmodels: Econometric and statistical modeling with python
Skipper Seabold and Josef Perktold · 2010
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Data Structures for Statistical Computing in Python
Wes McKinney · 2010
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Attention Is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Forecasting: principles and practice
Rob J Hyndman and George Athanasopoulos · 2018
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Deep state space models for time series forecasting
Syama Sundar Rangapuram, Matthias W Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski · 2018
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Efficient forecasting of electricity spot prices with expert and lasso models
Bartosz Uniejewski and Rafał Weron · 2018
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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High-dimensional multivariate forecasting with low-rank gaussian copula processes
David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, and Jan Gasthaus · 2019
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GluonTS: Probabilistic and Neural Time Series Modeling in Python
Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, et al · 2020
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Array programming with NumPy
Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant · 2020
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The M4 Competition: 100,000 time series and 61 forecasting methods
Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos · 2020
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N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio · 2020
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Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks
Bernardo Pérez Orozco and Stephen J. Roberts · 2020
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pandas-dev/pandas: Pandas, February 2020
The pandas development team · 2020
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A simple combination of univariate models
Fotios Petropoulos and Ivan Svetunkov · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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Weatherbench: a benchmark data set for data-driven weather forecasting
Stephan Rasp, Peter D Dueben, Sebastian Scher, Jonathan A Weyn, Soukayna Mouatadid, and Nils Thuerey · 2020
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Deepar: Probabilistic forecasting with autoregressive recurrent networks
David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
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Vivit: A video vision transformer
Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid · 2021
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Lag-llama: Towards foundation models for time series forecasting, 2023
Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Marin Biloš, Hena Ghonia, Nadhir Vincent Hassen, Anderson Schneider, Sahil Garg, Alexandre Drouin, Nicolas Chapados, Yuriy Nevmyvaka, and Irina Rish · 2023
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Causal inference for time series
Jakob Runge, Andreas Gerhardus, Gherardo Varando, Veronika Eyring, and Gustau Camps-Valls · 2023
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Autogluon–timeseries: Automl for probabilistic time series forecasting
Oleksandr Shchur, Ali Caner Turkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, and Bernie Wang · 2023
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Llama 2: Open Foundation and Fine-Tuned Chat Models, 2023
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom · 2023
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A methodology for validating diversity in synthetic time series generation
Fouad Bahrpeyma, Mark Roantree, Paolo Cappellari, Michael Scriney, and Andrew McCarren · 2021
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Monash Time Series Forecasting Archive
Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb, Rob J. Hyndman, and Pablo Montero-Manso · 2021
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Temporal fusion transformers for interpretable multi-horizon time series forecasting
Bryan Lim, Sercan Ö Arık, Nicolas Loeff, and Tomas Pfister · 2021
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Meta-learning framework with applications to zero-shot time-series forecasting
Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio · 2021
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Msa transformer
Roshan M Rao, Jason Liu, Robert Verkuil, Joshua Meier, John Canny, Pieter Abbeel, Tom Sercu, and Alexander Rives · 2021
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Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting
Kashif Rasul, Calvin Seward, Ingmar Schuster, and Roland Vollgraf · 2021
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StatsForecast: Lightning fast forecasting with statistical and econometric models
Federico Garza, Max Mergenthaler Canseco, Cristian Challú, and Kin G. Olivares · 2022
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Pushing the limits of pre-training for time series forecasting in the cloudops domain
Gerald Woo, Chenghao Liu, Akshat Kumar, and Doyen Sahoo · 2023
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Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting
Yunhao Zhang and Junchi Yan · 2023
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Gift-Eval: A benchmark for general time series forecasting model evaluation
Taha Aksu, Gerald Woo, Juncheng Liu, Xu Liu, Chenghao Liu, Silvio Savarese, Caiming Xiong, and Doyen Sahoo · 2024
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Chronos: Learning the language of time series
Abdul Fatir Ansari, Lorenzo Stella, Ali Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Hao Wang, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, and Bernie Wang · 2024
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Timegpt-1, 2024
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Time-LLM: Time series forecasting by reprogramming large language models
Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, and Qingsong Wen · 2024
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Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu · 2024
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Unified training of universal time series forecasting transformers
Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, and Doyen Sahoo · 2024
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Efficient streaming language models with attention sinks
Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis · 2024
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Towards long-context time series foundation models
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This time is different: An observability perspective on time series foundation models
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