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Time series classification plays a fundamental role in a wide range of real-world applications.
Inceptiontime: Finding alexnet for time series classification
Hassan Ismail Fawaz, Benjamin Lucas, Germain Forestier, Charlotte Pelletier, Daniel F Schmidt, Jonathan Weber, Geoffrey I Webb, Lhassane Idoumghar, Pierre-Alain Muller, and François Petitjean. 2020 · 1962
Earlier work this paper cites.
Statistical comparisons of classifiers over multiple data sets
Janez Demšar. 2006 · 2006
Earlier work this paper cites.
Experiencing SAX: a novel symbolic representation of time series
Jessica Lin, Eamonn Keogh, Li Wei, and Stefano Lonardi. 2007 · 2007
Earlier work this paper cites.
Querying and mining of time series data: experimental comparison of representations and distance measures
Hui Ding, Goce Trajcevski, Peter Scheuermann, Xiaoyue Wang, and Eamonn Keogh. 2008 · 2008
Earlier work this paper cites.
Dynamic time warping constraint learning for large margin nearest neighbor classification
Daren Yu, Xiao Yu, Qinghua Hu, Jinfu Liu, and Anqi Wu. 2011 · 2011
Earlier work this paper cites.
A shapelet transform for time series classification. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining . 289–297
Jason Lines, Luke M Davis, Jon Hills, and Anthony Bagnall. 2012 · 2012
Earlier work this paper cites.
A time series forest for classification and feature extraction
Houtao Deng, George Runger, Eugene Tuv, and Martyanov Vladimir. 2013 · 2013
Earlier work this paper cites.
Learning time-series shapelets. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining . 392–401
Josif Grabocka, Nicolas Schilling, Martin Wistuba, and Lars Schmidt-Thieme. 2014 · 2014
Earlier work this paper cites.
Urban computing: concepts, methodologies, and applications
Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. 2014a · 2014
Earlier work this paper cites.
Multi-scale convolutional neural networks for time series classification
Zhicheng Cui, Wenlin Chen, and Yixin Chen. 2016 · 2016
Earlier work this paper cites.
Service usage classification with encrypted internet traffic in mobile messaging apps
Yanjie Fu, Hui Xiong, Xinjiang Lu, Jin Yang, and Can Chen. 2016 · 2016
Earlier work this paper cites.
WaveNet: A Generative Model for Raw Audio. In Proc. SSW 2016 . 125–125
Aäron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. 2016 · 2016
Earlier work this paper cites.
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
Anthony Bagnall, Jason Lines, Aaron Bostrom, James Large, and Eamonn Keogh. 2017 · 2017
Earlier work this paper cites.
The UEA multivariate time series classification archive, 2018
Anthony Bagnall, Hoang Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn Keogh. 2018 · 2018
Earlier work this paper cites.
Auto: Scaling deep reinforcement learning for datacenter-scale automatic traffic optimization. In Proceedings of the 2018 conference of the ACM special interest group on data communication . 191–205
Li Chen, Justinas Lingys, Kai Chen, and Feng Liu. 2018 · 2018
Earlier work this paper cites.
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018 · 2018
Earlier work this paper cites.
Learning compact recurrent neural networks with block-term tensor decomposition. In Proceedings of the IEEE conference on computer vision and pattern recognition . 9378–9387
Jinmian Ye, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, and Zenglin Xu. 2018 · 2018
Earlier work this paper cites.
Joint representation learning for multi-modal transportation recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 1036–1043
Hao Liu, Ting Li, Renjun Hu, Yanjie Fu, Jingjing Gu, and Hui Xiong. 2019 · 2019
Earlier work this paper cites.
Reformer: The Efficient Transformer. In International Conference on Learning Representations
Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. 2020 · 2020
Earlier work this paper cites.
Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey
Flora D Salim, Bing Dong, Mohamed Ouf, Qi Wang, Ilaria Pigliautile, Xuyuan Kang, Tianzhen Hong, Wenbo Wu, Yapan Liu, Shakila Khan Rumi, et al · 2020
Earlier work this paper cites.
Semi-supervised hierarchical recurrent graph neural network for city-wide parking availability prediction. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 34. 1186–1193
Weijia Zhang, Hao Liu, Yanchi Liu, Jingbo Zhou, and Hui Xiong. 2020 · 2020
Cited alongside, same era.
Minirocket: A very fast (almost) deterministic transform for time series classification. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery and data mining . 248–257
Angus Dempster, Daniel F Schmidt, and Geoffrey I Webb. 2021 · 2021
Cited alongside, same era.
Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery and data mining . 547–555
Liangzhe Han, Bowen Du, Leilei Sun, Yanjie Fu, Yisheng Lv, and Hui Xiong. 2021 · 2021
Cited alongside, same era.
Hubert: Self-supervised speech representation learning by masked prediction of hidden units
Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, and Abdelrahman Mohamed. 2021 · 2021
Cited alongside, same era.
Tempo: Prompt-based generative pre-trained transformer for time series forecasting. In International Conference on Learning Representations
Defu Cao, Furong Jia, Sercan O Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, and Yan Liu. 2024 · 2024
Closest in time.
Exploring the potential of large language models (llms) in learning on graphs
Zhikai Chen, Haitao Mao, Hang Li, Wei Jin, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu, et al · 2024
Closest in time.
Exploring large-scale language models to evaluate eeg-based multimodal data for mental health. In Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing . 412–417
Yongquan Hu, Shuning Zhang, Ting Dang, Hong Jia, Flora D Salim, Wen Hu, and Aaron J Quigley. 2024 · 2024
Closest in time.
Gpt4mts: Prompt-based large language model for multimodal time-series forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 23343–23351
Furong Jia, Kevin Wang, Yixiang Zheng, Defu Cao, and Yan Liu. 2024 · 2024
Closest in time.
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
Cited alongside, same era.
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
Alejandro Pasos Ruiz, Michael Flynn, James Large, Matthew Middlehurst, and Anthony Bagnall. 2021 · 2021
Cited alongside, same era.
A transformer-based framework for multivariate time series representation learning. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery and data mining . 2114–2124
George Zerveas, Srideepika Jayaraman, Dhaval Patel, Anuradha Bhamidipaty, and Carsten Eickhoff. 2021 · 2021
Cited alongside, same era.
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. In International Conference on Learning Representations
Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. 2022 · 2022
Cited alongside, same era.
Urban traffic dynamics prediction—a continuous spatial-temporal meta-learning approach
Yingxue Zhang, Yanhua Li, Xun Zhou, Jun Luo, and Zhi-Li Zhang. 2022 · 2022
Cited alongside, same era.
Formertime: Hierarchical multi-scale representations for multivariate time series classification. In Proceedings of the ACM web conference 2023 . 1437–1445
Mingyue Cheng, Qi Liu, Zhiding Liu, Zhi Li, Yucong Luo, and Enhong Chen. 2023 · 2023
Cited alongside, same era.
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
Guangyin Jin, Yuxuan Liang, Yuchen Fang, Zezhi Shao, Jincai Huang, Junbo Zhang, and Yu Zheng. 2023b · 2023
Cited alongside, same era.
Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning . PMLR, 19730–19742
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023 · 2023
Cited alongside, same era.
Empowering time series analysis with large language models: A survey. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence . 8095–8103
Yushan Jiang, Zijie Pan, Xikun Zhang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, and Dongjin Song. 2024 · 2024
Closest in time.
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models. In International Conference on Learning Representations
Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-fang Li, Shirui Pan, et al · 2024
Closest in time.
Foundation models for time series analysis: A tutorial and survey. In Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining . 6555–6565
Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shirui Pan, and Qingsong Wen. 2024 · 2024
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Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2024b · 2024
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Unitime: A language-empowered unified model for cross-domain time series forecasting. In Proceedings of the ACM on Web Conference 2024 . 4095–4106
Xu Liu, Junfeng Hu, Yuan Li, Shizhe Diao, Yuxuan Liang, Bryan Hooi, and Roger Zimmermann. 2024a · 2024
Closest in time.
aeon: a Python toolkit for learning from time series
Matthew Middlehurst, Ali Ismail-Fawaz, Antoine Guillaume, Christopher Holder, David Guijo-Rubio, Guzal Bulatova, Leonidas Tsaprounis, Lukasz Mentel, Martin Walter, Patrick Schäfer, et al · 2024
Closest in time.
S 2 S^{2} IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting. In Forty-first International Conference on Machine Learning
Zijie Pan, Yushan Jiang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, and Dongjin Song. 2024 · 2024
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Towards urban general intelligence: A review and outlook of urban foundation models
Weijia Zhang, Jindong Han, Zhao Xu, Hang Ni, Hao Liu, and Hui Xiong. 2024a · 2024
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Graph spatiotemporal process for multivariate time series anomaly detection with missing values
Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, Haishuai Wang, Khoa T Phan, Yi-Ping Phoebe Chen, Shirui Pan, and Wei Xiang. 2024 · 2024
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Convtimenet: A deep hierarchical fully convolutional model for multivariate time series analysis. In Companion Proceedings of the ACM on Web Conference 2025 . 171–180
Mingyue Cheng, Jiqian Yang, Tingyue Pan, Qi Liu, Zhi Li, and Shijin Wang. 2025b · 2025
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Foundation models and intelligent decision-making: Progress, challenges, and perspectives
Jincai Huang, Yongjun Xu, Qi Wang, Qi Cheems Wang, Xingxing Liang, Fei Wang, Zhao Zhang, Wei Wei, Boxuan Zhang, Libo Huang, et al · 2025
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Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
Yucong Luo, Yitong Zhou, Mingyue Cheng, Jiahao Wang, Daoyu Wang, Tingyue Pan, and Jintao Zhang. 2025 · 2025
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Xiaoyu Tao, Shilong Zhang, Mingyue Cheng, Daoyu Wang, Tingyue Pan, Bokai Pan, Changqing Zhang, and Shijin Wang. 2025 · 2025
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Can slow-thinking llms reason over time? empirical studies in time series forecasting
Jiahao Wang, Mingyue Cheng, and Qi Liu. 2025 · 2025
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TimeEmb: A Lightweight Static-Dynamic Disentanglement Framework for Time Series Forecasting. In The Thirty-ninth Annual Conference on Neural Information Processing Systems
Mingyuan Xia, Chunxu Zhang, Zijian Zhang, Hao Miao, Qidong Liu, Yuanshao Zhu, and Bo Yang. 2025 · 2025
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