{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T20:27:15Z","timestamp":1787689635507,"version":"build-2784847793"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,9]]},"DOI":"10.1145\/3770854.3780318","type":"proceedings-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T12:07:40Z","timestamp":1785499660000},"page":"1928-1939","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["From Tokenizer Bias to Backbone Capability: A Controlled Study of LLMs for Time Series Forecasting"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7779-5097","authenticated-orcid":false,"given":"Xinyu","family":"Zhang","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, Shenzhen, China and Hong Kong Polytechnic University, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6161-9232","authenticated-orcid":false,"given":"Shanshan","family":"Feng","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8816-3856","authenticated-orcid":false,"given":"Xutao","family":"Li","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3372-2267","authenticated-orcid":false,"given":"Kenghong","family":"Lin","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3929-6625","authenticated-orcid":false,"given":"Fan","family":"Li","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic University, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7681-2327","authenticated-orcid":false,"given":"Pengfei","family":"Jia","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, et al.","author":"Ansari Abdul Fatir","year":"2024","unstructured":"Abdul Fatir Ansari, Lorenzo Stella, Ali Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, et al., 2024. Chronos: Learning the Language of Time Series. Transactions on Machine Learning Research (2024)."},{"key":"e_1_3_2_2_2_1","volume-title":"Multi-patch prediction: Adapting llms for time series representation learning. arXiv preprint arXiv:2402.04852","author":"Bian Yuxuan","year":"2024","unstructured":"Yuxuan Bian, Xuan Ju, Jiangtong Li, Zhijian Xu, Dawei Cheng, and Qiang Xu. 2024. Multi-patch prediction: Adapting llms for time series representation learning. arXiv preprint arXiv:2402.04852 (2024)."},{"key":"e_1_3_2_2_3_1","volume-title":"Time series analysis: forecasting and control","author":"Box George EP","unstructured":"George EP Box, Gwilym M Jenkins, Gregory C Reinsel, and Greta M Ljung. 2015. Time series analysis: forecasting and control. John Wiley & Sons."},{"key":"e_1_3_2_2_4_1","volume-title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting. In The Twelfth International Conference on Learning Representations.","author":"Cao Defu","year":"2024","unstructured":"Defu Cao, Furong Jia, Sercan O Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, and Yan Liu. 2024. TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_2_2_5_1","volume-title":"Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms. CoRR","author":"Chang Ching","year":"2023","unstructured":"Ching Chang, Wen-Chih Peng, and Tien-Fu Chen. 2023. Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms. CoRR (2023)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599533"},{"key":"e_1_3_2_2_7_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Gruver Nate","year":"2024","unstructured":"Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew G Wilson. 2024. Large language models are zero-shot time series forecasters. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01043"},{"key":"e_1_3_2_2_9_1","volume-title":"Context-Alignment: Activating and Enhancing LLMs Capabilities in Time Series. In The Thirteenth International Conference on Learning Representations.","author":"Hu Yuxiao","year":"2025","unstructured":"Yuxiao Hu, Qian Li, Dongxiao Zhang, Jinyue Yan, and Yuntian Chen. 2025. Context-Alignment: Activating and Enhancing LLMs Capabilities in Time Series. In The Thirteenth International Conference on Learning Representations."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"Furong Jia Kevin Wang Yixiang Zheng Defu Cao and Yan Liu. 2024. GPT4MTS: prompt-based large language model for multimodal time-series forecasting. In Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence. 23343-23351.","DOI":"10.1609\/aaai.v38i21.30383"},{"key":"e_1_3_2_2_11_1","volume-title":"International Conference on Learning Representations.","author":"Jin Ming","year":"2024","unstructured":"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. Time-LLM: Time Series Forecasting by Reprogramming Large Language Models. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE65448.2025.00237"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i18.34067"},{"key":"e_1_3_2_2_14_1","volume-title":"Taming pre-trained llms for generalised time series forecasting via cross-modal knowledge distillation. CoRR","author":"Liu Peiyuan","year":"2024","unstructured":"Peiyuan Liu, Hang Guo, Tao Dai, Naiqi Li, Jigang Bao, Xudong Ren, Yong Jiang, and Shu-Tao Xia. 2024a. Taming pre-trained llms for generalised time series forecasting via cross-modal knowledge distillation. CoRR (2024)."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645434"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.52202\/079017-3882"},{"key":"e_1_3_2_2_17_1","volume-title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models. In Forty-first International Conference on Machine Learning.","author":"Liu Yong","year":"2025","unstructured":"Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, and Mingsheng Long. 2025c. Timer: Generative Pre-trained Transformers Are Large Time Series Models. In Forty-first International Conference on Machine Learning."},{"key":"e_1_3_2_2_18_1","volume-title":"The Twelfth International Conference on Learning Representations.","author":"Luo Donghao","year":"2024","unstructured":"Donghao Luo and Xue Wang. 2024. Moderntcn: A modern pure convolution structure for general time series analysis. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_2_2_19_1","volume-title":"Conference on Robot Learning. PMLR, 2498-2518","author":"Mirchandani Suvir","year":"2023","unstructured":"Suvir Mirchandani, Fei Xia, Pete Florence, Brian Ichter, Danny Driess, Montserrat Gonzalez Arenas, Kanishka Rao, Dorsa Sadigh, and Andy Zeng. 2023. Large Language Models as General Pattern Machines. In Conference on Robot Learning. PMLR, 2498-2518."},{"key":"e_1_3_2_2_20_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Nie Yuqi","year":"2022","unstructured":"Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. 2022. A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_2_21_1","volume-title":"Proceedings of the 41st International Conference on Machine Learning. 39135-39153","author":"Pan Zijie","year":"2024","unstructured":"Zijie Pan, Yushan Jiang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, and Dongjin Song. 2024. S2IP-LLM: semantic space informed prompt learning with LLM for time series forecasting. In Proceedings of the 41st International Conference on Machine Learning. 39135-39153."},{"key":"e_1_3_2_2_22_1","unstructured":"Alec Radford Jeffrey Wu Rewon Child David Luan Dario Amodei Ilya Sutskever et al. 2019. Language models are unsupervised multitask learners. OpenAI blog Vol. 1 8 (2019) 9."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2019.07.001"},{"key":"e_1_3_2_2_24_1","first-page":"83314","article-title":"Scaling law for time series forecasting","volume":"37","author":"Shi Jingzhe","year":"2024","unstructured":"Jingzhe Shi, Qinwei Ma, Huan Ma, and Lei Li. 2024. Scaling law for time series forecasting. Advances in Neural Information Processing Systems, Vol. 37 (2024), 83314-83344.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_25_1","volume-title":"The Twelfth International Conference on Learning Representations.","author":"Sun Chenxi","year":"2024","unstructured":"Chenxi Sun, Hongyan Li, Yaliang Li, and Shenda Hong. 2024. TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_2_2_26_1","volume-title":"The Thirty-eighth Annual Conference on Neural Information Processing Systems.","author":"Tan Mingtian","year":"2024","unstructured":"Mingtian Tan, Mike A Merrill, Vinayak Gupta, Tim Althoff, and Thomas Hartvigsen. 2024. Are language models actually useful for time series forecasting?. In The Thirty-eighth Annual Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_27_1","unstructured":"Hugo Touvron Thibaut Lavril Gautier Izacard Xavier Martinet Marie-Anne Lachaux Timothee Lacroix Baptiste Rozi\u00e8re Naman Goyal Eric Hambro Faisal Azhar et al. 2023. LLaMA: Open and Efficient Foundation Language Models. (2023)."},{"key":"e_1_3_2_2_28_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Wang Huiqiang","year":"2022","unstructured":"Huiqiang Wang, Jian Peng, Feihu Huang, Jince Wang, Junhui Chen, and Yifei Xiao. 2022. Micn: Multi-scale local and global context modeling for long-term series forecasting. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_2_29_1","volume-title":"Using Pre-trained LLMs for Multivariate Time Series Forecasting. arXiv preprint arXiv:2501.06386","author":"Wolff Malcolm L","year":"2025","unstructured":"Malcolm L Wolff, Shenghao Yang, Kari Torkkola, and Michael W Mahoney. 2025. Using Pre-trained LLMs for Multivariate Time Series Forecasting. arXiv preprint arXiv:2501.06386 (2025)."},{"key":"e_1_3_2_2_30_1","first-page":"53140","article-title":"Unified Training of Universal Time Series Forecasting Transformers","author":"Woo Gerald","year":"2024","unstructured":"Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, and Doyen Sahoo. 2024. Unified Training of Universal Time Series Forecasting Transformers. In International Conference on Machine Learning. PMLR, 53140-53164.","journal-title":"International Conference on Machine Learning. PMLR"},{"key":"e_1_3_2_2_31_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Wu Haixu","year":"2022","unstructured":"Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. 2022. TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_2_32_1","volume-title":"Promptcast: A new prompt-based learning paradigm for time series forecasting","author":"Xue Hao","year":"2023","unstructured":"Hao Xue and Flora D Salim. 2023. Promptcast: A new prompt-based learning paradigm for time series forecasting. IEEE Transactions on Knowledge and Data Engineering (2023)."},{"key":"e_1_3_2_2_33_1","volume-title":"Towards Neural Scaling Laws for Time Series Foundation Models. In The Thirteenth International Conference on Learning Representations.","author":"Yao Qingren","year":"2025","unstructured":"Qingren Yao, Chao-Han Huck Yang, Renhe Jiang, Yuxuan Liang, Ming Jin, and Shirui Pan. 2025. Towards Neural Scaling Laws for Time Series Foundation Models. In The Thirteenth International Conference on Learning Representations."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671713"},{"key":"e_1_3_2_2_35_1","volume-title":"Enhancing time series forecasting via multi-level text alignment with llms. arXiv preprint arXiv:2504.07360","author":"Zhao Taibiao","year":"2025","unstructured":"Taibiao Zhao, Xiaobing Chen, and Mingxuan Sun. 2025. Enhancing time series forecasting via multi-level text alignment with llms. arXiv preprint arXiv:2504.07360 (2025)."},{"key":"e_1_3_2_2_36_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. Advances in neural information processing systems Vol. 36 (2023) 43322-43355.","DOI":"10.52202\/075280-1877"}],"event":{"name":"KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Jeju Island Republic of Korea","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3770854.3780318","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T20:16:38Z","timestamp":1787688998000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770854.3780318"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,20]]},"references-count":36,"alternative-id":["10.1145\/3770854.3780318","10.1145\/3770854"],"URL":"https:\/\/doi.org\/10.1145\/3770854.3780318","relation":{},"subject":[],"published":{"date-parts":[[2026,4,20]]},"assertion":[{"value":"2026-04-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}