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Recent studies have indicated that vision models pre-trained on images can serve as time series foundation models (TSFMs) by reformulating time series forecasting (TSF) as image reconstruction.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Classification of time-series images using deep convolutional neural networks
Nima Hatami, Yann Gavet, and Johan Debayle · 2018
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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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Deepar: Probabilistic forecasting with autoregressive recurrent networks
Valentin Flunkert, David Salinas, and Jan Gasthaus · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Characterizing and avoiding negative transfer
Zirui Wang, Zihang Dai, Barnabás Póczos, and Jaime Carbonell · 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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Forecasting with time series imaging
Xixi Li, Yanfei Kang, and Feng Li · 2020
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Beit: Bert pre-training of image transformers
Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 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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Ast: Audio spectrogram transformer
Yuan Gong, Yu-An Chung, and James Glass · 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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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Visual time series forecasting: an image-driven approach
Srijan Sood, Zhen Zeng, Naftali Cohen, Tucker Balch, and Manuela Veloso · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long · 2021
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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
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Generative time series forecasting with diffusion, denoise, and disentanglement
Yan Li, Xinjiang Lu, Yaqing Wang, and Dejing Dou · 2022
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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 · 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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Long-term forecasting with tide: Time-series dense encoder
Abhimanyu Das, Weihao Kong, Andrew Leach, Shaan Mathur, Rajat Sen, and Rose Yu · 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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A decoder-only foundation model for time-series forecasting
Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou · 2024
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Timesiam: A pre-training framework for siamese time-series modeling
Jiaxiang Dong, Haixu Wu, Yuxuan Wang, Yunzhong Qiu, Li Zhang, Jianmin Wang, and Mingsheng Long · 2024
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Cheng Feng, Long Huang, and Denis Krompass · 2024
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Are synthetic time-series data really not as good as real data?
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Alex Fang, Albin Madappally Jose, Amit Jain, Ludwig Schmidt, Alexander Toshev, and Vaishaal Shankar · 2023
Cited alongside, same era.
Large language models are zero-shot time series forecasters
Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew G Wilson · 2023
Cited alongside, same era.
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, et al · 2023
Cited alongside, same era.
Time series as images: Vision transformer for irregularly sampled time series
Zekun Li, Shiyang Li, and Xifeng Yan · 2023
Cited alongside, same era.
When less is more: Investigating data pruning for pretraining llms at scale
Max Marion, Ahmet Üstün, Luiza Pozzobon, Alex Wang, Marzieh Fadaee, and Sara Hooker · 2023
Cited alongside, same era.
Filtering, distillation, and hard negatives for vision-language pre-training
Filip Radenovic, Abhimanyu Dubey, Abhishek Kadian, Todor Mihaylov, Simon Vandenhende, Yash Patel, Yi Wen, Vignesh Ramanathan, and Dhruv Mahajan · 2023
Cited alongside, same era.
Image-based time series forecasting: A deep convolutional neural network approach
Artemios-Anargyros Semenoglou, Evangelos Spiliotis, and Vassilios Assimakopoulos · 2023
Cited alongside, same era.
Fanzhe Fu, Junru Chen, Jing Zhang, Carl Yang, Lvbin Ma, and Yang Yang · 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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Scaling laws for data filtering–data curation cannot be compute agnostic
Sachin Goyal, Pratyush Maini, Zachary C Lipton, Aditi Raghunathan, and J Zico Kolter · 2024
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Unitime: A language-empowered unified model for cross-domain time series forecasting
Xu Liu, Junfeng Hu, Yuan Li, Shizhe Diao, Yuxuan Liang, Bryan Hooi, and Roger Zimmermann · 2024
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The rise of diffusion models in time-series forecasting
Caspar Meijer and Lydia Y Chen · 2024
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Utilizing image transforms and diffusion models for generative modeling of short and long time series
Ilan Naiman, Nimrod Berman, Itai Pemper, Idan Arbiv, Gal Fadlon, and Omri Azencot · 2024
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Time-moe: Billion-scale time series foundation models with mixture of experts
Xiaoming Shi, Shiyu Wang, Yuqi Nie, Dianqi Li, Zhou Ye, Qingsong Wen, and Ming Jin · 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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Vitime: A visual intelligence-based foundation model for time series forecasting
Luoxiao Yang, Yun Wang, Xinqi Fan, Israel Cohen, Jingdong Chen, Yue Zhao, and Zijun Zhang · 2024
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Multi-modal time series analysis: A tutorial and survey
Yushan Jiang, Kanghui Ning, Zijie Pan, Xuyang Shen, Jingchao Ni, Wenchao Yu, Anderson Schneider, Haifeng Chen, Yuriy Nevmyvaka, and Dongjin Song · 2025
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Harnessing vision models for time series analysis: A survey
Jingchao Ni, Ziming Zhao, ChengAo Shen, Hanghang Tong, Dongjin Song, Wei Cheng, Dongsheng Luo, and Haifeng Chen · 2025
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Multi-modal view enhanced large vision models for long-term time series forecasting
ChengAo Shen, Wenchao Yu, Ziming Zhao, Dongjin Song, Wei Cheng, Haifeng Chen, and Jingchao Ni · 2025
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Beyond numbers: A survey of time series analysis in the era of multimodal llms
Xiongxiao Xu, Yue Zhao, S Yu Philip, and Kai Shu · 2025
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