{"type":"ScholarlyArticle","title":"Large Language Models for Time Series: A Survey","authors":[{"name":"Xiyuan Zhang","slug":"xiyuan-zhang","given":"Xiyuan","family":"Zhang","affiliation":"University of California , San Diego"},{"name":"Ranak Roy Chowdhury","slug":"ranak-roy-chowdhury","given":"Ranak Roy","family":"Chowdhury","affiliation":"University of California , San Diego"},{"name":"Rajesh K. Gupta","slug":"rajesh-k-gupta","given":"Rajesh K.","family":"Gupta","affiliation":"University of California , San Diego"},{"name":"Jingbo Shang","slug":"jingbo-shang","given":"Jingbo","family":"Shang","affiliation":"University of California , San Diego"}],"abstract":"Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision. Going beyond text, image and graphics, LLMs present a significant potential for analysis of time series data, benefiting domains such as climate, IoT, healthcare, traffic, audio and finance. This survey paper provides an in-depth exploration and a detailed taxonomy of the various methodologies employed to harness the power of LLMs for time series analysis. We address the inherent challenge of bridging the gap between LLMs' original text data training and the numerical nature of time series data, and explore strategies for transferring and distilling knowledge from LLMs to numerical time series analysis. We detail various methodologies, including (1) direct prompting of LLMs, (2) time series quantization, (3) aligning techniques, (4) utilization of the vision modality as a bridging mechanism, and (5) the combination of LLMs with tools. Additionally, this survey offers a comprehensive overview of the existing multimodal time series and text datasets in diverse domains, and discusses the challenges and future opportunities of this emerging field.","doi":"10.24963/ijcai.2024/921","url":"https://ijcai.org/proceedings/2024/921","pdf":"https://ijcai.org/proceedings/2024/0921.pdf","markdown":"https://ijcai.org/proceedings/2024/921.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/921","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Survey Track","track":"Survey Track","number":921,"pages":{"first":8335,"last":8343},"keywords":["Machine Learning: ML: Time series and data streams","Data Mining: DM: Mining spatial and/or temporal data","Natural Language Processing: NLP: Language models"],"videos":[],"references":[{"position":1,"raw":"[Agostinelli et al., 2023] Andrea Agostinelli, Timo I Denk, Zalán Borsos, Jesse Engel, Mauro Verzetti, Antoine Cail- lon, Qingqing Huang, Aren Jansen, Adam Roberts, Marco Tagliasacchi, et al. Musiclm: Generating music from text. arXiv:2301.11325, 2023.","title":"Musiclm: Generating music from text","authors":"Andrea Agostinelli, I Timo, Zalán Denk, Jesse Borsos, Mauro Engel, Antoine Verzetti, Qingqing Caillon, Aren Huang, Adam Jansen, Marco Roberts, Tagliasacchi","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Ardila et al., 2019] Rosana Ardila, Megan Branson, Kelly Davis, Michael Henretty, Michael Kohler, Josh Meyer, Reuben Morais, Lindsay Saunders, Francis M Tyers, and Gregor Weber. Common voice: A massively-multilingual speech corpus. arXiv:1912.06670, 2019.","title":"Common voice: A massively-multilingual speech corpus","authors":"Rosana Ardila, Megan Branson, Kelly Davis, Michael Henretty, Michael Kohler, Josh Meyer, Reuben Morais, Lindsay Saunders, Francis M Tyers, Gregor Weber","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cai et al., 2023] Yifu Cai, Mononito Goswami, Arjun Choudhry, Arvind Srinivasan, and Artur Dubrawski. Jolt: Jointly learned representations of language and time- series. In NeurIPS Deep Generative Models for Health Workshop, 2023.","title":"Jolt: Jointly learned representations of language and timeseries","authors":"Yifu Cai, Mononito Goswami, Arjun Choudhry, Arvind Srinivasan, Artur Dubrawski","year":"2023","venue":"NeurIPS Deep Generative Models for Health Workshop","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cao et al., 2023] Defu Cao, Furong Jia, Sercan O Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, and Yan Liu. Tempo: Prompt-based generative pre-trained transformer for time series forecasting. arXiv:2310.04948, 2023.","title":"Tempo: Prompt-based generative pre-trained transformer for time series forecasting","authors":"Defu Cao, Furong Jia, O Sercan, Tomas Arik, Yixiang Pfister, Wen Zheng, Yan Ye, Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chang et al., 2023] Ching Chang, Wen-Chih Peng, and Tien-Fu Chen. Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms. arXiv:2308.08469, 2023.","title":"Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms","authors":"Ching Chang, Wen-Chih Peng, Tien-Fu Chen","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chung et al., 2023] Hyunseung Chung, Jiho Kim, Joon- myoung Kwon, Ki-Hyun Jeon, Min Sung Lee, and Edward Choi. Text-to-ecg: 12-lead electrocardiogram synthesis conditioned on clinical text reports. In ICASSP, pages 1- 5. IEEE, 2023.","title":"Text-to-ecg: 12-lead electrocardiogram synthesis conditioned on clinical text reports","authors":"Hyunseung Chung, Jiho Kim, Joonmyoung Kwon, Ki-Hyun Jeon, Min Sung Lee, Edward Choi","year":"2023","venue":"ICASSP","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Ding et al., 2023] Yujie Ding, Shuai Jia, Tianyi Ma, Bingcheng Mao, Xiuze Zhou, Liuliu Li, and Dongming Han. Integrating stock features and global information via large language models for enhanced stock return predic- tion. arXiv:2310.05627, 2023.","title":"Integrating stock features and global information via large language models for enhanced stock return prediction","authors":"Yujie Ding, Shuai Jia, Tianyi Ma, Bingcheng Mao, Xiuze Zhou, Liuliu Li, Dongming Han","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Duan et al., 2023] Yiqun Duan, Charles Zhou, Zhen Wang, Yu-Kai Wang, and Chin-teng Lin. Dewave: Discrete en- coding of eeg waves for eeg to text translation. In NeurIPS, 2023.","title":"Dewave: Discrete encoding of eeg waves for eeg to text translation","authors":"Yiqun Duan, Charles Zhou, Zhen Wang, Yu-Kai Wang, Chin-Teng Lin","year":"2023","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Girdhar et al., 2023] Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Ar- mand Joulin, and Ishan Misra. Imagebind: One embed- ding space to bind them all. In CVPR, pages 15180-15190, 2023.","title":"Imagebind: One embedding space to bind them all","authors":"Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, Ishan Misra","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Gong et al., 2023] Yuan Gong, Hongyin Luo, Alexander H Liu, Leonid Karlinsky, and James Glass. Listen, think, and understand. arXiv:2305.10790, 2023.","title":"Listen, think, and understand","authors":"Yuan Gong, Hongyin Luo, Alexander H Liu, Leonid Karlinsky, James Glass","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Graule and Isler, 2023] Moritz A Graule and Volkan Isler. Gg-llm: Geometrically grounding large language models for zero-shot human activity forecasting in human-aware task planning. arXiv:2310.20034, 2023.","title":"Gg-llm: Geometrically grounding large language models for zero-shot human activity forecasting in human-aware task planning","authors":"A Moritz, Volkan Graule, Isler","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Grauman et al., 2022] Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Ro- hit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, et al. Ego4d: Around the world in 3,000 hours of egocentric video. In CVPR, pages 18995-19012, 2022.","title":"Ego4d: Around the world in 3,000 hours of egocentric video","authors":"Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Gruver et al., 2023] Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew Gordon Wilson. Large language models are zero-shot time series forecasters. arXiv:2310.07820, 2023.","title":"Large language models are zero-shot time series forecasters","authors":"Gruver","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Han et al., 2022] William Han, Jielin Qiu, Jiacheng Zhu, Mengdi Xu, Douglas Weber, Bo Li, and Ding Zhao. An empirical exploration of cross-domain alignment between language and electroencephalogram. arXiv:2208.06348, 2022.","title":"An empirical exploration of cross-domain alignment between language and electroencephalogram","authors":"William Han, Jielin Qiu, Jiacheng Zhu, Mengdi Xu, Douglas Weber, Bo Li, Ding Zhao","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Hollenstein et al., 2019] Nora Hollenstein, Marius Troen- dle, Ce Zhang, and Nicolas Langer. Zuco 2.0: A dataset of physiological recordings during natural reading and an- notation. arXiv:1912.00903, 2019.","title":"Zuco 2.0: A dataset of physiological recordings during natural reading and annotation","authors":"Nora Hollenstein, Marius Troendle, Ce Zhang, Nicolas Langer","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Jin et al., 2023a] Ming Jin, Shiyu Wang, Lintao Ma, Zhix- uan Chu, James Y Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, et al. Time-llm: Time series forecasting by reprogramming large language models. arXiv:2310.01728, 2023.","title":"Time-llm: Time series forecasting by reprogramming large language models","authors":"Jin","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Jin et al., 2023b] Ming Jin, Qingsong Wen, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, Xiaoli Li, et al. Large models for time series and spatio-temporal data: A survey and out- look. arXiv:2310.10196, 2023.","title":"Large models for time series and spatio-temporal data: A survey and outlook","authors":"Jin","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[King et al., 2023] Ryan King, Tianbao Yang, and Bobak J Mortazavi. Multimodal pretraining of medical time series and notes. In ML4H, pages 244-255. PMLR, 2023.","title":"Multimodal pretraining of medical time series and notes","authors":"Ryan King, Tianbao Yang, Bobak J Mortazavi","year":"2023","venue":"ML4H","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lee et al., 2023] Geon Lee, Wenchao Yu, Wei Cheng, and Haifeng Chen. Moat: Multi-modal augmented time series forecasting. 2023.","title":"Moat: Multi-modal augmented time series forecasting","authors":"Geon Lee, Wenchao Yu, Wei Cheng, Haifeng Chen","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Leng et al., 2023] Zikang Leng, Hyeokhyen Kwon, and Thomas Plötz. Generating virtual on-body accelerometer data from virtual textual descriptions for human activity recognition. arXiv:2305.03187, 2023.","title":"Generating virtual on-body accelerometer data from virtual textual descriptions for human activity recognition","authors":"Leng","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Li et al., 2023] Junkai Li, Weizhi Ma, and Yang Liu. Modeling time series as text sequence a frequency- vectorization transformer for time series forecasting. 2023.","title":"Modeling time series as text sequence a frequencyvectorization transformer for time series forecasting","authors":"Li","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Liu et al., 2023a] Che Liu, Zhongwei Wan, Sibo Cheng, Mi Zhang, and Rossella Arcucci. Etp: Learning transferable ecg representations via ecg-text pre-training. arXiv:2309.07145, 2023.","title":"Etp: Learning transferable ecg representations via ecg-text pre-training","authors":"Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Liu et al., 2023b] Xin Liu, Daniel McDuff, Geza Kovacs, Isaac Galatzer-Levy, Jacob Sunshine, Jiening Zhan, Ming- Zher Poh, Shun Liao, Paolo Di Achille, and Shwetak Pa- tel. Large language models are few-shot health learners. arXiv:2305.15525, 2023.","title":"Large language models are few-shot health learners","authors":"Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Ma et al., 2023] Qianli Ma, Zhen Liu, Zhenjing Zheng, Ziyang Huang, Siying Zhu, Zhongzhong Yu, and James T Kwok. A survey on time-series pre-trained models. arXiv:2305.10716, 2023.","title":"A survey on time-series pre-trained models","authors":"Qianli Ma, Zhen Liu, Zhenjing Zheng, Ziyang Huang, Siying Zhu, Zhongzhong Yu, James T Kwok","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Moon et al., 2022] Seungwhan Moon, Andrea Madotto, Zhaojiang Lin, Alireza Dirafzoon, Aparajita Saraf, Amy Bearman, and Babak Damavandi. Imu2clip: Multimodal contrastive learning for imu motion sensors from egocen- tric videos and text. arXiv:2210.14395, 2022.","title":"Imu2clip: Multimodal contrastive learning for imu motion sensors from egocentric videos and text","authors":"Moon","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Moon et al., 2023] Seungwhan Moon, Andrea Madotto, Zhaojiang Lin, Tushar Nagarajan, Matt Smith, Shashank Jain, Chun-Fu Yeh, Prakash Murugesan, Peyman Heidari, Yue Liu, et al. Anymal: An efficient and scalable any- modality augmented language model. arXiv:2309.16058, 2023.","title":"Anymal: An efficient and scalable anymodality augmented language model","authors":"Moon","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Oh et al., 2023] Jungwoo Oh, Seongsu Bae, Gyubok Lee, Joon-myoung Kwon, and Edward Choi. Ecg-qa: A comprehensive question answering dataset combined with electrocardiogram. arXiv:2306.15681, 2023.","title":"Ecg-qa: A comprehensive question answering dataset combined with electrocardiogram","authors":"Oh","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Qin et al., 2023] Yujia Qin, Shihao Liang, Yining Ye, Kun- lun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al. Toolllm: Facilitating large language models to master 16000+ real-world apis. arXiv:2307.16789, 2023.","title":"Toolllm: Facilitating large language models to master 16000+ real-world apis","authors":"Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Qiu et al., 2023] Jielin Qiu, William Han, Jiacheng Zhu, Mengdi Xu, Michael Rosenberg, Emerson Liu, Dou- glas Weber, and Ding Zhao. Transfer knowledge from natural language to electrocardiography: Can we de- tect cardiovascular disease through language models? arXiv:2301.09017, 2023.","title":"Transfer knowledge from natural language to electrocardiography: Can we detect cardiovascular disease through language models?","authors":"Qiu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Rubenstein et al., 2023] Paul K Rubenstein, Chulayuth Asawaroengchai, Duc Dung Nguyen, Ankur Bapna, Zalán Borsos, Félix de Chaumont Quitry, Peter Chen, Dalia El Badawy, Wei Han, Eugene Kharitonov, et al. Au- diopalm: A large language model that can speak and listen. arXiv:2306.12925, 2023.","title":"Audiopalm: A large language model that can speak and listen","authors":"Chulayuth Paul K Rubenstein, Asawaroengchai, Dung Duc, Ankur Nguyen, Zalán Bapna, Félix Borsos, De Chaumont, Peter Quitry, Dalia El Chen, Wei Badawy, Eugene Han, Kharitonov","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Su et al., 2023] Yixuan Su, Tian Lan, Huayang Li, Jialu Xu, Yan Wang, and Deng Cai. Pandagpt: One model to instruction-follow them all. arXiv:2305.16355, 2023.","title":"Pandagpt: One model to instruction-follow them all","authors":"Yixuan Su, Tian Lan, Huayang Li, Jialu Xu, Yan Wang, Deng Cai","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Sun et al., 2023] Chenxi Sun, Yaliang Li, Hongyan Li, and Shenda Hong. Test: Text prototype aligned embedding to activate llm's ability for time series. arXiv:2308.08241, 2023.","title":"Test: Text prototype aligned embedding to activate llm's ability for time series","authors":"Chenxi Sun, Yaliang Li, Hongyan Li, Shenda Hong","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Talukder and Gkioxari, 2023] Sabera J Talukder and Geor- gia Gkioxari. Time series modeling at scale: A universal representation across tasks and domains. 2023.","title":"Time series modeling at scale: A universal representation across tasks and domains","authors":"J Sabera, Georgia Talukder, Gkioxari","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Tang et al., 2023] Changli Tang, Wenyi Yu, Guangzhi Sun, Xianzhao Chen, Tian Tan, Wei Li, Lu Lu, Zejun Ma, and Chao Zhang. Salmonn: Towards generic hearing abilities for large language models. arXiv:2310.13289, 2023.","title":"Salmonn: Towards generic hearing abilities for large language models","authors":"Changli Tang, Wenyi Yu, Guangzhi Sun, Xianzhao Chen, Tian Tan, Wei Li, Lu Lu, Zejun Ma, Chao Zhang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Wagner et al., 2020] Patrick Wagner, Nils Strodthoff, Ralf- Dieter Bousseljot, Dieter Kreiseler, Fatima I Lunze, Woj- ciech Samek, and Tobias Schaeffter. Ptb-xl, a large pub- licly available electrocardiography dataset. Scientific data, 7(1):154, 2020.","title":"Ptb-xl, a large publicly available electrocardiography dataset","authors":"Patrick Wagner, Nils Strodthoff, Ralf-Dieter Bousseljot, Dieter Kreiseler, Fatima I Lunze, Wojciech Samek, Tobias Schaeffter","year":"2020","venue":"Scientific data","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Wang and Ji, 2022] Zhenhailong Wang and Heng Ji. Open vocabulary electroencephalography-to-text decoding and zero-shot sentiment classification. In AAAI, 2022.","title":"Open vocabulary electroencephalography-to-text decoding and zero-shot sentiment classification","authors":"Wang, Ji ;, Zhenhailong Wang, Heng Ji","year":"2022","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wimmer and Rekabsaz, 2023] Christopher Wimmer and Navid Rekabsaz. Leveraging vision-language models for granular market change prediction. arXiv:2301.10166, 2023.","title":"Leveraging vision-language models for granular market change prediction","authors":"Christopher Wimmer, Navid Rekabsaz","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wu et al., 2023] Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prab- hanjan Kambadur, David Rosenberg, and Gideon Mann. Bloomberggpt: A large language model for finance. arXiv:2303.17564, 2023.","title":"Bloomberggpt: A large language model for finance","authors":"Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, Gideon Mann","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Xie et al., 2023a] Qianqian Xie, Weiguang Han, Yanzhao Lai, Min Peng, and Jimin Huang. The wall street neo- phyte: A zero-shot analysis of chatgpt over multimodal stock movement prediction challenges. arXiv:2304.05351, 2023.","title":"The wall street neophyte: A zero-shot analysis of chatgpt over multimodal stock movement prediction challenges","authors":"Xie","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Xie et al., 2023b] Qianqian Xie, Weiguang Han, Xiao Zhang, Yanzhao Lai, Min Peng, Alejandro Lopez-Lira, and Jimin Huang. Pixiu: A large language model, instruction data and evaluation benchmark for finance. arXiv:2306.05443, 2023.","title":"Pixiu: A large language model, instruction data and evaluation benchmark for finance","authors":"Xie","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Xing et al., 2021] Tianwei Xing, Luis Garcia, Federico Cerutti, Lance Kaplan, Alun Preece, and Mani Srivastava. Deepsqa: Understanding sensor data via question answer- ing. In IoTDI, 2021.","title":"Deepsqa: Understanding sensor data via question answering","authors":"Tianwei Xing, Luis Garcia, Federico Cerutti, Lance Kaplan, Alun Preece, Mani Srivastava","year":"2021","venue":"IoTDI","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Xue and Salim, 2022] Hao Xue and Flora D Salim. Prompt- cast: A new prompt-based learning paradigm for time se- ries forecasting. 2022.","title":"Promptcast: A new prompt-based learning paradigm for time series forecasting","authors":"Hao Xue, Flora D Salim","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Yang et al., 2023] Dongchao Yang, Jinchuan Tian, Xu Tan, Rongjie Huang, Songxiang Liu, Xuankai Chang, Jiatong Shi, Sheng Zhao, Jiang Bian, Xixin Wu, et al. Uniaudio: An audio foundation model toward universal audio gener- ation. arXiv:2310.00704, 2023.","title":"Uniaudio: An audio foundation model toward universal audio generation","authors":"Yang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Yu et al., 2023] Xinli Yu, Zheng Chen, Yuan Ling, Shu- jing Dong, Zongyi Liu, and Yanbin Lu. Temporal data meets llm-explainable financial time series forecasting. arXiv:2306.11025, 2023.","title":"Temporal data meets llm-explainable financial time series forecasting","authors":"Xinli Yu, Zheng Chen, Yuan Ling, Shujing Dong, Zongyi Liu, Yanbin Lu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Yun et al., 2019] Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J Reddi, and Sanjiv Ku- mar. Are transformers universal approximators of sequence-to-sequence functions? arXiv:1912.10077, 2019.","title":"Are transformers universal approximators of sequence-to-sequence functions","authors":"Yun","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zhang et al., 2023a] Xiyuan Zhang, Ranak Roy Chowd- hury, Jiayun Zhang, Dezhi Hong, Rajesh K Gupta, and Jingbo Shang. Unleashing the power of shared label struc- tures for human activity recognition. In CIKM, pages 3340-3350, 2023.","title":"Unleashing the power of shared label structures for human activity recognition","authors":"Zhang","year":"2023","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Zhang et al., 2023b] Yunkai Zhang, Yawen Zhang, Ming Zheng, Kezhen Chen, Chongyang Gao, Ruian Ge, Siyuan Teng, Amine Jelloul, Jinmeng Rao, Xiaoyuan Guo, et al. Insight miner: A time series analysis dataset for cross- domain alignment with natural language. In NeurIPS AI4Science, 2023.","title":"Insight miner: A time series analysis dataset for crossdomain alignment with natural language","authors":"Zhang","year":"2023","venue":"NeurIPS AI4Science","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Zhong et al., 2023] Ziyuan Zhong, Davis Rempe, Yuxiao Chen, Boris Ivanovic, Yulong Cao, Danfei Xu, Marco Pavone, and Baishakhi Ray. Language-guided traffic sim- ulation via scene-level diffusion. arXiv:2306.06344, 2023.","title":"Language-guided traffic simulation via scene-level diffusion","authors":"Ziyuan Zhong, Davis Rempe, Yuxiao Chen, Boris Ivanovic, Yulong Cao, Danfei Xu, Marco Pavone, Baishakhi Ray","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Zhou et al., 2023a] Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, and Rong Jin. One fits all: Power general time series analysis by pretrained lm. arXiv:2302.11939, 2023.","title":"One fits all: Power general time series analysis by pretrained lm","authors":"Zhou","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Zhou et al., 2023b] Yunjiao Zhou, Jianfei Yang, Han Zou, and Lihua Xie. Tent: Connect language mod- els with iot sensors for zero-shot activity recognition. arXiv:2311.08245, 2023.","title":"Tent: Connect language models with iot sensors for zero-shot activity recognition","authors":"Zhou","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}