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Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection. arXiv preprint arXiv:2405.15370 (2024)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_3_2_1_33_1","volume-title":"LLM Knows Geometry Better than Algebra: Numerical Understanding of LLM-Based Agents in A Trading Arena. arXiv preprint arXiv:2502.17967","author":"Ma Tianmi","year":"2025","unstructured":"Tianmi Ma, Jiawei Du, Wenxin Huang, Wenjie Wang, Liang Xie, Xian Zhong, and Joey Tianyi Zhou. 2025. LLM Knows Geometry Better than Algebra: Numerical Understanding of LLM-Based Agents in A Trading Arena. arXiv preprint arXiv:2502.17967 (2025)."},{"key":"e_1_3_2_1_34_1","volume-title":"Strada-LLM: Graph LLM for traffic prediction. arXiv preprint arXiv:2410.20856","author":"Moghadas Seyed Mohamad","year":"2024","unstructured":"Seyed Mohamad Moghadas, Yangxintong Lyu, Bruno Cornelis, Alexandre Alahi, and Adrian Munteanu. 2024. 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LLM-based Online Prediction of Time-varying Graph Signals. arXiv preprint arXiv:2410.18718 (2024)."},{"key":"e_1_3_2_1_37_1","volume-title":"Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Marin Bilo\u0161, Hena Ghonia, Nadhir Hassen, Anderson Schneider, et al.","author":"Rasul Kashif","year":"2023","unstructured":"Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Marin Bilo\u0161, Hena Ghonia, Nadhir Hassen, Anderson Schneider, et al., 2023. Lag-llama: Towards foundation models for time series forecasting. In R0-FoMo: Robustness of Few-shot and Zero-shot Learning in Large Foundation Models."},{"key":"e_1_3_2_1_38_1","volume-title":"Support vector method for novelty detection. Advances in neural information processing systems","author":"Sch\u00f6lkopf Bernhard","year":"1999","unstructured":"Bernhard Sch\u00f6lkopf, Robert C Williamson, Alex Smola, John Shawe-Taylor, and John Platt. 1999. Support vector method for novelty detection. Advances in neural information processing systems, Vol. 12 (1999)."},{"key":"e_1_3_2_1_39_1","volume-title":"Timeseries anomaly detection using temporal hierarchical one-class network. Advances in neural information processing systems","author":"Shen Lifeng","year":"2020","unstructured":"Lifeng Shen, Zhuocong Li, and James Kwok. 2020. Timeseries anomaly detection using temporal hierarchical one-class network. Advances in neural information processing systems, Vol. 33 (2020), 13016-13026."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330672"},{"key":"e_1_3_2_1_41_1","volume-title":"Ryan Burnell, Libin Bai, Anmol Gulati, Garrett Tanzer, Damien Vincent, Zhufeng Pan, Shibo Wang, et al.","author":"Team Gemini","year":"2024","unstructured":"Gemini Team, Petko Georgiev, Ving Ian Lei, Ryan Burnell, Libin Bai, Anmol Gulati, Garrett Tanzer, Damien Vincent, Zhufeng Pan, Shibo Wang, et al., 2024. 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Number Cookbook: Number Understanding of Language Models and How to Improve It. arXiv preprint arXiv:2411.03766 (2024)."},{"key":"e_1_3_2_1_55_1","volume-title":"Ad-llm: Benchmarking large language models for anomaly detection. arXiv preprint arXiv:2412.11142","author":"Yang Tiankai","year":"2024","unstructured":"Tiankai Yang, Yi Nian, Shawn Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan Rossi, Kaize Ding, et al., 2024b. Ad-llm: Benchmarking large language models for anomaly detection. arXiv preprint arXiv:2412.11142 (2024)."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3691338"},{"key":"e_1_3_2_1_57_1","volume-title":"NeurIPS 2023 AI for Science Workshop.","author":"Zhang Yunkai","year":"2023","unstructured":"Yunkai Zhang, Yawen Zhang, Ming Zheng, Kezhen Chen, Chongyang Gao, Ruian Ge, Siyuan Teng, Amine Jelloul, Jinmeng Rao, Xiaoyuan Guo, et al., 2023. Insight miner: A time series analysis dataset for cross-domain alignment with natural language. In NeurIPS 2023 AI for Science Workshop."},{"key":"e_1_3_2_1_58_1","volume-title":"Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting. arXiv preprint arXiv:2502.04395","author":"Zhong Siru","year":"2025","unstructured":"Siru Zhong, Weilin Ruan, Ming Jin, Huan Li, Qingsong Wen, and Yuxuan Liang. 2025. Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting. arXiv preprint arXiv:2502.04395 (2025)."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"crossref","unstructured":"Tian Zhou Peisong Niu Liang Sun Rong Jin et al. 2023. One fits all: Power general time series analysis by pretrained lm. 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