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Deep learning for time series forecasting has seen significant advancements over the past decades.
Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton · 1991
Earlier work this paper cites.
Robust estimation of a location parameter
Peter J Huber · 1992
Earlier work this paper cites.
Forecastable component analysis
Georg Goerg · 2013
Earlier work this paper cites.
Time series analysis: forecasting and control
George EP Box, Gwilym M Jenkins, Gregory C Reinsel, and Greta M Ljung · 2015
Earlier work this paper cites.
Forecasting fine-grained air quality based on big data
Yu Zheng, Xiuwen Yi, Ming Li, Ruiyuan Li, Zhangqing Shan, Eric Chang, and Tianrui Li · 2015
Earlier work this paper cites.
Flu portal dashboard, 2017
CDC · 2017
Earlier work this paper cites.
The sparsely-gated mixture-of-experts layer
N Shazeer, A Mirhoseini, K Maziarz, A Davis, Q Le, G Hinton, and J Dean · 2017
Earlier work this paper cites.
Ashish Vaswani · 2017
Earlier work this paper cites.
Deep spatio-temporal residual networks for citywide crowd flows prediction
Junbo Zhang, Yu Zheng, and Dekang Qi · 2017
Earlier work this paper cites.
Deep state space models for time series forecasting
Syama Sundar Rangapuram, Matthias W Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski · 2018
Earlier work this paper cites.
Project tycho 2.0: a repository to improve the integration and reuse of data for global population health
Willem G van Panhuis, Anne Cross, and Donald S Burke · 2018
Earlier work this paper cites.
Beijing Multi-Site Air-Quality Data
Song Chen · 2019
Earlier work this paper cites.
Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting
Rajat Sen, Hsiang-Fu Yu, and Inderjit S Dhillon · 2019
Earlier work this paper cites.
RobustTrend: a huber loss with a combined first and second order difference regularization for time series trend filtering
Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, and Jian Tan · 2019
Earlier work this paper cites.
Root mean square layer normalization
Biao Zhang and Rico Sennrich · 2019
Earlier work this paper cites.
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, Lorenzo Stella, Ali Caner Türkmen, and Yuyang Wang · 2020
Earlier work this paper cites.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
Earlier work this paper cites.
Gshard: Scaling giant models with conditional computation and automatic sharding
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen · 2020
Earlier work this paper cites.
N-beats: Neural basis expansion analysis for interpretable time series forecasting
Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
Deepar: Probabilistic forecasting with autoregressive recurrent networks
David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski · 2020
Earlier work this paper cites.
Glu variants improve transformer
Noam Shazeer · 2020
Earlier work this paper cites.
Monash time series forecasting archive
Rakshitha Wathsadini Godahewa, Christoph Bergmeir, Geoffrey I. Webb, Rob Hyndman, and Pablo Montero-Manso · 2021
Earlier work this paper cites.
Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, and Alexandra Peste · 2021
Earlier work this paper cites.
Temporal fusion transformers for interpretable multi-horizon time series forecasting
Bryan Lim, Sercan Ö Arık, Nicolas Loeff, and Tomas Pfister · 2021
Earlier work this paper cites.
A machine learning approach for forecasting hierarchical time series
Paolo Mancuso, Veronica Piccialli, and Antonio M Sudoso · 2021
Earlier work this paper cites.
Scaling vision with sparse mixture of experts
Carlos Riquelme, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, and Neil Houlsby · 2021
Earlier work this paper cites.
Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long · 2021
Cited alongside, same era.
A transformer-based framework for multivariate time series representation learning
George Zerveas, Srideepika Jayaraman, Dhaval Patel, Anuradha Bhamidipaty, and Carsten Eickhoff · 2021
Cited alongside, same era.
Informer: Beyond efficient transformer for long sequence time-series forecasting
Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang · 2021
Cited alongside, same era.
Revisiting neural scaling laws in language and vision
Ibrahim M Alabdulmohsin, Behnam Neyshabur, and Xiaohua Zhai · 2022
Cited alongside, same era.
Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer · 2022
Cited alongside, same era.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Later among the works it cites.
Transformers in time series: a survey
Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun · 2023
Later among the works it cites.
Pushing the limits of pre-training for time series forecasting in the cloudops domain
Gerald Woo, Chenghao Liu, Akshat Kumar, and Doyen Sahoo · 2023
Later among the works it cites.
Are transformers effective for time series forecasting?
Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu · 2023
Later among the works it cites.
Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting
Yunhao Zhang and Junchi Yan · 2023
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Multivariate time series forecasting with dynamic graph neural odes
Ming Jin, Yu Zheng, Yuan-Fang Li, Siheng Chen, Bin Yang, and Shirui Pan · 2022
Cited alongside, same era.
End-to-end modeling of hierarchical time series using autoregressive transformer and conditional normalizing flow-based reconciliation
Shiyu Wang, Fan Zhou, Yinbo Sun, Lintao Ma, James Zhang, and Yangfei Zheng · 2022
Cited alongside, same era.
Ts2vec: Towards universal representation of time series
Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, and Bixiong Xu · 2022
Cited alongside, same era.
Self-supervised contrastive pre-training for time series via time-frequency consistency
Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, and Marinka Zitnik · 2022
Cited alongside, same era.
Sdwpf: A dataset for spatial dynamic wind power forecasting challenge at kdd cup 2022
Jingbo Zhou, Xinjiang Lu, Yixiong Xiao, Jiantao Su, Junfu Lyu, Yanjun Ma, and Dejing Dou · 2022
Cited alongside, same era.
FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting
Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin · 2022
Cited alongside, same era.
Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al · 2023
Cited alongside, same era.
Chronos: Learning the language of time series
Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, et al · 2024
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Pathformer: Multi-scale transformers with adaptive pathways for time series forecasting
Peng Chen, Yingying Zhang, Yunyao Cheng, Yang Shu, Yihang Wang, Qingsong Wen, Bin Yang, and Chenjuan Guo · 2024
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Deepseekmoe: Towards ultimate expert specialization in mixture-of-experts language models
Damai Dai, Chengqi Deng, Chenggang Zhao, RX Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, Y Wu, et al · 2024
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FlashAttention-2: Faster attention with better parallelism and work partitioning
Tri Dao · 2024
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A decoder-only foundation model for time-series forecasting
Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou · 2024
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Heterogeneity-informed meta-parameter learning for spatiotemporal time series forecasting
Zheng Dong, Renhe Jiang, Haotian Gao, Hangchen Liu, Jinliang Deng, Qingsong Wen, and Xuan Song · 2024
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
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Moment: A family of open time-series foundation models
Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, and Artur Dubrawski · 2024
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Attractor memory for long-term time series forecasting: A chaos perspective
Jiaxi Hu, Yuehong Hu, Wei Chen, Ming Jin, Shirui Pan, Qingsong Wen, and Yuxuan Liang · 2024
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Position: What can large language models tell us about time series analysis
Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, and Qingsong Wen · 2024
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Foundation models for time series analysis: A tutorial and survey
Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shirui Pan, and Qingsong Wen · 2024
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Sparsetsf: Modeling long-term time series forecasting with 1k parameters
Shengsheng Lin, Weiwei Lin, Wentai Wu, Haojun Chen, and Junjie Yang · 2024
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Time series analysis for education: Methods, applications, and future directions
Shengzhong Mao, Chaoli Zhang, Yichi Song, Jindong Wang, Xiao-Jun Zeng, Zenglin Xu, and Qingsong Wen · 2024
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Mixture-of-linear-experts for long-term time series forecasting
Ronghao Ni, Zinan Lin, Shuaiqi Wang, and Giulia Fanti · 2024
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A survey of large language models for financial applications: Progress, prospects and challenges
Yuqi Nie, Yaxuan Kong, Xiaowen Dong, John M Mulvey, H Vincent Poor, Qingsong Wen, and Stefan Zohren · 2024
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Shiyi Qi, Zenglin Xu, Yiduo Li, Liangjian Wen, Qingsong Wen, Qifan Wang, and Yuan Qi · 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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Neuralreconciler for hierarchical time series forecasting
Shiyu Wang · 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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Self-supervised learning for time series analysis: Taxonomy, progress, and prospects
Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Y Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, et al · 2024
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Timemixer++: A general time series pattern machine for universal predictive analysis
Shiyu Wang, Jiawei Li, Xiaoming Shi, Zhou Ye, Baichuan Mo, Wenze Lin, Shengtong Ju, Zhixuan Chu, and Ming Jin · 2025
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Towards neural scaling laws for time series foundation models
Qingren Yao, Chao-Han Huck Yang, Renhe Jiang, Yuxuan Liang, Ming Jin, and Shirui Pan · 2025
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