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Large language models (LLMs) have demonstrated their effectiveness in multivariate time series classification (MTSC).
Are language models actually useful for time series forecasting?. In Proceedings of the 38th International Conference on Neural Information Processing Systems (Vancouver, BC, Canada) (NIPS ’24) . Curran Associates Inc., Red Hook, NY, USA, Article 1922, 30 pages
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Deep learning for time series classification: a review
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Self-Consistency Improves Chain of Thought Reasoning in Language Models. In The Eleventh International Conference on Learning Representations
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Time Series Classification Method Based on Multi-Scale Convolution with LSTM. In 2023 IEEE 11th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) , Vol. 11. 1738–1744
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One fits all: Power general time series analysis by pretrained lm
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Large language models (LLMs) on tabular data: Prediction, generation, and understanding — a survey
Xi Fang, Weijie Xu, Fiona Anting Tan, Jiani Zhang, Ziqing Hu, Yanjun (Jane) Qi, Scott Nickleach, Diego Socolinsky, "SHS" Srinivasan Sengamedu, and Christos Faloutsos. 2024 · 2024
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MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (Virtual Event, Singapore) (KDD ’21) . Association for Computing Machinery, New York, NY, USA, 248–257
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HIVE-COTE 2.0: a new meta ensemble for time series classification
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The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
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Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence , Vol. 35. 11106–11115
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A Reinforcement Learning-Informed Pattern Mining Framework for Multivariate Time Series Classification. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22 , Lud De Raedt (Ed.). International Joint Conferences on Artificial Intelligence Organization, 2994–3000
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Empowering time series analysis with large language models: a survey. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (Jeju, Korea) (IJCAI ’24) . Article 895, 9 pages
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Time-LLM: Time Series Forecasting by Reprogramming Large Language Models. In The Twelfth International Conference on Learning Representations
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LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting. In Findings of the Association for Computational Linguistics: ACL 2024 , Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.). Association for Computational Linguistics, Bangkok, Thailand, 7832–7840
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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
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Large language models: A survey
Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Amatriain, and Jianfeng Gao. 2024 · 2024
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Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification
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Understanding the weakness of large language model agents within a complex android environment. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 6061–6072
Mingzhe Xing, Rongkai Zhang, Hui Xue, Qi Chen, Fan Yang, and Zhen Xiao. 2024 · 2024
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PromptCast: A New Prompt-Based Learning Paradigm for Time Series Forecasting
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A survey on large language model (llm) security and privacy: The good, the bad, and the ugly
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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 (Sydney NSW, Australia) (WWW ’25) . Association for Computing Machinery, New York, NY, USA, 171–180
Mingyue Cheng, Jiqian Yang, Tingyue Pan, Qi Liu, Zhi Li, and Shijin Wang. 2025c · 2025
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TIME-FFM: towards LM-empowered federated foundation model for time series forecasting. In Proceedings of the 38th International Conference on Neural Information Processing Systems (Vancouver, BC, Canada) (NIPS ’24) . Curran Associates Inc., Red Hook, NY, USA, Article 2996, 27 pages
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