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Time series has been left behind in the era of pre-training and transfer learning.
Scoring rules for continuous probability distributions
James E Matheson and Robert L Winkler · 1976
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A learning algorithm for continually running fully recurrent neural networks
Ronald J Williams and David Zipser · 1989
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Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
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Automatic time series forecasting: the forecast package for r
Rob J Hyndman and Yeasmin Khandakar · 2008
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statsmodels: Econometric and statistical modeling with python
Skipper Seabold and Josef Perktold · 2010
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More Google cluster data
John Wilkes · 2011
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Robust random cut forest based anomaly detection on streams
Sudipto Guha, Nina Mishra, Gourav Roy, and Okke Schrijvers · 2016
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Resource central: Understanding and predicting workloads for improved resource management in large cloud platforms
Eli Cortez, Anand Bonde, Alexandre Muzio, Mark Russinovich, Marcus Fontoura, and Ricardo Bianchini · 2017
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Automatic anomaly detection in the cloud via statistical learning
Jordan Hochenbaum, Owen S Vallis, and Arun Kejariwal · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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A multi-horizon quantile recurrent forecaster
Ruofeng Wen, Kari Torkkola, Balakrishnan Narayanaswamy, and Dhruv Madeka · 2017
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Forecasting: principles and practice
Rob J Hyndman and George Athanasopoulos · 2018
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Statistical and machine learning forecasting methods: Concerns and ways forward
Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos · 2018
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Optuna: A next-generation hyperparameter optimization framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon · 2019
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Probabilistic forecasting with spline quantile function rnns
Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, and Tim Januschowski · 2019
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Who limits the resource efficiency of my datacenter: An analysis of alibaba datacenter traces
Jing Guo, Zihao Chang, Sa Wang, Haiyang Ding, Yihui Feng, Liang Mao, and Yungang Bao · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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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, et al · 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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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Normalizing kalman filters for multivariate time series analysis
Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella, Hilaf Hasson, Patrick Gallinari, and Tim Januschowski · 2020
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Intelligent virtual machine provisioning in cloud computing
Chuan Luo, Bo Qiao, Xin Chen, Pu Zhao, Randolph Yao, Hongyu Zhang, Wei Wu, Andrew Zhou, and Qingwei Lin · 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
Cited alongside, same era.
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
Cited alongside, same era.
Non-stationary transformers: Exploring the stationarity in time series forecasting
Yong Liu, Haixu Wu, Jianmin Wang, and Mingsheng Long · 2022
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Learning quantile functions without quantile crossing for distribution-free time series forecasting
Youngsuk Park, Danielle Maddix, François-Xavier Aubet, Kelvin Kan, Jan Gasthaus, and Yuyang Wang · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Ts2vec: Towards universal representation of time series
Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, and Bixiong Xu · 2022
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Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting
Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin · 2022
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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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On layer normalization in the transformer architecture
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, and Tieyan Liu · 2020
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Pot: Python optimal transport
Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, and Titouan Vayer · 2021
Cited alongside, same era.
Monash time series forecasting archive
Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb, Rob J. Hyndman, and Pablo Montero-Manso · 2021
Cited alongside, same era.
Temporal fusion transformers for interpretable multi-horizon time series forecasting
Bryan Lim, Sercan Ö Arık, Nicolas Loeff, and Tomas Pfister · 2021
Cited alongside, same era.
Meta-learning framework with applications to zero-shot time-series forecasting
Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio · 2021
Cited alongside, same era.
Investigating the accuracy of cross-learning time series forecasting methods
Artemios-Anargyros Semenoglou, Evangelos Spiliotis, Spyros Makridakis, and Vassilios Assimakopoulos · 2021
Cited alongside, same era.
Ching Chang, Wen-Chih Peng, and Tien-Fu Chen · 2023
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Ai for it operations (aiops) on cloud platforms: Reviews, opportunities and challenges, 2023
Qian Cheng, Doyen Sahoo, Amrita Saha, Wenzhuo Yang, Chenghao Liu, Gerald Woo, Manpreet Singh, Silvio Saverese, and Steven C. H. Hoi · 2023
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Mitigating cold-start forecasting using cold causal demand forecasting model
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Gaussian error linear units (gelus), 2023
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Forecast evaluation for data scientists: common pitfalls and best practices
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ForecastPFN: Universal forecasting for healthcare
Gurnoor Singh Khurana, Samuel Dooley, Siddartha Venkat Naidu, and Colin White · 2023
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A survey on time-series pre-trained models
Qianli Ma, Zhen Liu, Zhenjing Zheng, Ziyang Huang, Siying Zhu, Zhongzhong Yu, and James T Kwok · 2023
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A time series is worth 64 words: Long-term forecasting with transformers
Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam · 2023
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Codegen: An open large language model for code with multi-turn program synthesis
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Generative agents: Interactive simulacra of human behavior, 2023
Joon Sung Park, Joseph C. O’Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Learning deep time-index models for time series forecasting
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Fits: Modeling time series with 10 k 10k parameters
Zhijian Xu, Ailing Zeng, and Qiang Xu · 2023
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Are transformers effective for time series forecasting?
Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu · 2023
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One fits all: Power general time series analysis by pretrained lm
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