{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T05:13:17Z","timestamp":1778389997140,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100000855","name":"University Of Birmingham","doi-asserted-by":"publisher","award":["2226150"],"award-info":[{"award-number":["2226150"]}],"id":[{"id":"10.13039\/501100000855","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/Y002539\/1, EP\/T022221\/1, EP\/W032244\/1"],"award-info":[{"award-number":["EP\/Y002539\/1, EP\/T022221\/1, EP\/W032244\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,10]]},"DOI":"10.1145\/3746252.3761278","type":"proceedings-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T23:59:18Z","timestamp":1762559958000},"page":"4498-4508","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["BALM-TSF: Balanced Multimodal Alignment for LLM-Based Time Series Forecasting"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3515-2864","authenticated-orcid":false,"given":"Shiqiao","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Computer Science, University of Birmingham, Birmingham, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9341-538X","authenticated-orcid":false,"given":"Holger","family":"Sch\u00f6ner","sequence":"additional","affiliation":[{"name":"Siemens AG, Munich, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7007-6333","authenticated-orcid":false,"given":"Huanbo","family":"Lyu","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Birmingham, Birmingham, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0157-7648","authenticated-orcid":false,"given":"Edouard","family":"Fouch\u00e9","sequence":"additional","affiliation":[{"name":"Siemens AG, Nuremberg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1380-6428","authenticated-orcid":false,"given":"Shuo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Birmingham, Birmingham, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al.","author":"Achiam Josh","year":"2023","unstructured":"Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al., 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)."},{"key":"e_1_3_2_2_2_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_3_1","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems Vol. 33 (2020) 1877-1901."},{"key":"e_1_3_2_2_4_1","volume-title":"Tempo: Prompt-based generative pre-trained transformer for time series forecasting. arXiv preprint arXiv:2310.04948","author":"Cao Defu","year":"2023","unstructured":"Defu Cao, Furong Jia, Sercan O Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, and Yan Liu. 2023. Tempo: Prompt-based generative pre-trained transformer for time series forecasting. arXiv preprint arXiv:2310.04948 (2023)."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2016.12.015"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00626-4"},{"key":"e_1_3_2_2_7_1","first-page":"2327","article-title":"Deep learning for event-driven stock prediction","volume":"15","author":"Ding Xiao","year":"2015","unstructured":"Xiao Ding, Yue Zhang, Ting Liu, and Junwen Duan. 2015. Deep learning for event-driven stock prediction.. In Ijcai, Vol. 15. 2327-2333.","journal-title":"Ijcai"},{"key":"e_1_3_2_2_8_1","volume-title":"Can LLMs Serve As Time Series Anomaly Detectors? arXiv preprint arXiv:2408.03475","author":"Dong Manqing","year":"2024","unstructured":"Manqing Dong, Hao Huang, and Longbing Cao. 2024. Can LLMs Serve As Time Series Anomaly Detectors? arXiv preprint arXiv:2408.03475 (2024)."},{"key":"e_1_3_2_2_9_1","volume-title":"Transformer language models without positional encodings still learn positional information. arXiv preprint arXiv:2203.16634","author":"Haviv Adi","year":"2022","unstructured":"Adi Haviv, Ori Ram, Ofir Press, Peter Izsak, and Omer Levy. 2022. Transformer language models without positional encodings still learn positional information. arXiv preprint arXiv:2203.16634 (2022)."},{"key":"e_1_3_2_2_10_1","volume-title":"Long short-term memory. Neural computation","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735-1780."},{"key":"e_1_3_2_2_11_1","volume-title":"Context-Alignment: Activating and Enhancing LLM Capabilities in Time Series. arXiv preprint arXiv:2501.03747","author":"Hu Yuxiao","year":"2025","unstructured":"Yuxiao Hu, Qian Li, Dongxiao Zhang, Jinyue Yan, and Yuntian Chen. 2025. Context-Alignment: Activating and Enhancing LLM Capabilities in Time Series. arXiv preprint arXiv:2501.03747 (2025)."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3703155"},{"key":"e_1_3_2_2_13_1","volume-title":"International conference on machine learning. PMLR, 4904-4916","author":"Jia Chao","year":"2021","unstructured":"Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. 2021. Scaling up visual and vision-language representation learning with noisy text supervision. In International conference on machine learning. PMLR, 4904-4916."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i21.30383"},{"key":"e_1_3_2_2_15_1","volume-title":"Time-llm: Time series forecasting by reprogramming large language models. arXiv preprint arXiv:2310.01728","author":"Jin Ming","year":"2023","unstructured":"Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, et al., 2023. Time-llm: Time series forecasting by reprogramming large language models. arXiv preprint arXiv:2310.01728 (2023)."},{"key":"e_1_3_2_2_16_1","volume-title":"Forty-first International Conference on Machine Learning.","author":"Jin Ming","year":"2024","unstructured":"Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, and Qingsong Wen. 2024. Position: What can large language models tell us about time series analysis. In Forty-first International Conference on Machine Learning."},{"key":"e_1_3_2_2_17_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=cGDAkQo1C0p","author":"Kim Taesung","year":"2021","unstructured":"Taesung Kim, Jinhee Kim, Yunwon Tae, Cheonbok Park, Jang-Ho Choi, and Jaegul Choo. 2021. Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=cGDAkQo1C0p"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210006"},{"key":"e_1_3_2_2_19_1","volume-title":"The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691","author":"Lester Brian","year":"2021","unstructured":"Brian Lester, Rami Al-Rfou, and Noah Constant. 2021. The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691 (2021)."},{"key":"e_1_3_2_2_20_1","first-page":"3843","article-title":"Solving quantitative reasoning problems with language models","volume":"35","author":"Lewkowycz Aitor","year":"2022","unstructured":"Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, et al., 2022. Solving quantitative reasoning problems with language models. Advances in Neural Information Processing Systems, Vol. 35 (2022), 3843-3857.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2968894"},{"key":"e_1_3_2_2_22_1","first-page":"29029","article-title":"Not all tokens are what you need for pretraining","volume":"37","author":"Lin Zhenghao","year":"2024","unstructured":"Zhenghao Lin, Zhibin Gou, Yeyun Gong, Xiao Liu, Ruochen Xu, Chen Lin, Yujiu Yang, Jian Jiao, Nan Duan, Weizhu Chen, et al., 2024. Not all tokens are what you need for pretraining. Advances in Neural Information Processing Systems, Vol. 37 (2024), 29029-29063.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_23_1","unstructured":"Aixin Liu Bei Feng Bing Xue Bingxuan Wang Bochao Wu Chengda Lu Chenggang Zhao Chengqi Deng Chenyu Zhang Chong Ruan et al. 2024a. Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437 (2024)."},{"key":"e_1_3_2_2_24_1","volume-title":"Timecma: Towards llm-empowered time series forecasting via cross-modality alignment. arXiv preprint arXiv:2406.01638","author":"Liu Chenxi","year":"2024","unstructured":"Chenxi Liu, Qianxiong Xu, Hao Miao, Sun Yang, Lingzheng Zhang, Cheng Long, Ziyue Li, and Rui Zhao. 2024c. Timecma: Towards llm-empowered time series forecasting via cross-modality alignment. arXiv preprint arXiv:2406.01638 (2024)."},{"key":"e_1_3_2_2_25_1","first-page":"77888","article-title":"Time-mmd: Multi-domain multimodal dataset for time series analysis","volume":"37","author":"Liu Haoxin","year":"2024","unstructured":"Haoxin Liu, Shangqing Xu, Zhiyuan Zhao, Lingkai Kong, Harshavardhan Prabhakar Kamarthi, Aditya Sasanur, Megha Sharma, Jiaming Cui, Qingsong Wen, Chao Zhang, et al., 2024d. Time-mmd: Multi-domain multimodal dataset for time series analysis. Advances in Neural Information Processing Systems, Vol. 37 (2024), 77888-77933.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_26_1","volume-title":"2024 e. Lstprompt: Large language models as zero-shot time series forecasters by long-short-term prompting. arXiv preprint arXiv:2402.16132","author":"Liu Haoxin","year":"2024","unstructured":"Haoxin Liu, Zhiyuan Zhao, Jindong Wang, Harshavardhan Kamarthi, and B Aditya Prakash. 2024 e. Lstprompt: Large language models as zero-shot time series forecasters by long-short-term prompting. arXiv preprint arXiv:2402.16132 (2024)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i18.34082"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645434"},{"key":"e_1_3_2_2_29_1","volume-title":"itransformer: Inverted transformers are effective for time series forecasting. arXiv preprint arXiv:2310.06625","author":"Liu Yong","year":"2023","unstructured":"Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, and Mingsheng Long. 2023. itransformer: Inverted transformers are effective for time series forecasting. arXiv preprint arXiv:2310.06625 (2023)."},{"key":"e_1_3_2_2_30_1","volume-title":"International Conference on Learning Representations.","author":"Nie Yuqi","year":"2023","unstructured":"Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. 2023. A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_31_1","volume-title":"Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748","author":"van den Oord Aaron","year":"2018","unstructured":"Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)."},{"key":"e_1_3_2_2_32_1","volume-title":"International conference on machine learning. PmLR, 8748-8763","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al., 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PmLR, 8748-8763."},{"key":"e_1_3_2_2_33_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_34_1","volume-title":"NumeroLogic: Number Encoding for Enhanced LLMs' Numerical Reasoning. arXiv preprint arXiv:2404.00459","author":"Schwartz Eli","year":"2024","unstructured":"Eli Schwartz, Leshem Choshen, Joseph Shtok, Sivan Doveh, Leonid Karlinsky, and Assaf Arbelle. 2024. NumeroLogic: Number Encoding for Enhanced LLMs' Numerical Reasoning. arXiv preprint arXiv:2404.00459 (2024)."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-020-00329-2"},{"key":"e_1_3_2_2_36_1","volume-title":"Tokenization counts: the impact of tokenization on arithmetic in frontier llms. arXiv preprint arXiv:2402.14903","author":"Singh Aaditya K","year":"2024","unstructured":"Aaditya K Singh and DJ Strouse. 2024. Tokenization counts: the impact of tokenization on arithmetic in frontier llms. arXiv preprint arXiv:2402.14903 (2024)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocae090"},{"key":"e_1_3_2_2_38_1","volume-title":"Test: Text prototype aligned embedding to activate llm's ability for time series. arXiv preprint arXiv:2308.08241","author":"Sun Chenxi","year":"2023","unstructured":"Chenxi Sun, Hongyan Li, Yaliang Li, and Shenda Hong. 2023. Test: Text prototype aligned embedding to activate llm's ability for time series. arXiv preprint arXiv:2308.08241 (2023)."},{"key":"e_1_3_2_2_39_1","first-page":"60162","article-title":"Are language models actually useful for time series forecasting","volume":"37","author":"Tan Mingtian","year":"2024","unstructured":"Mingtian Tan, Mike Merrill, Vinayak Gupta, Tim Althoff, and Tom Hartvigsen. 2024. Are language models actually useful for time series forecasting? Advances in Neural Information Processing Systems, Vol. 37 (2024), 60162-60191.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_40_1","volume-title":"Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971","author":"Touvron Hugo","year":"2023","unstructured":"Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth\u00e9e Lacroix, Baptiste Rozi\u00e8re, Naman Goyal, Eric Hambro, Faisal Azhar, et al., 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"crossref","unstructured":"Pengfei Wang Huanran Zheng Qi'ao Xu Silong Dai Yiqiao Wang Wenjing Yue Wei Zhu Tianwen Qian and Xiaoling Wang. 2025. TS-HTFA: Advancing Time Series Forecasting via Hierarchical Text-Free Alignment with Large Language Models. arXiv:2409.14978 [cs.AI] https:\/\/arxiv.org\/abs\/2409.14978","DOI":"10.3390\/sym17030401"},{"key":"e_1_3_2_2_42_1","volume-title":"Timexer: Empowering transformers for time series forecasting with exogenous variables. arXiv preprint arXiv:2402.19072","author":"Wang Yuxuan","year":"2024","unstructured":"Yuxuan Wang, Haixu Wu, Jiaxiang Dong, Guo Qin, Haoran Zhang, Yong Liu, Yunzhong Qiu, Jianmin Wang, and Mingsheng Long. 2024. Timexer: Empowering transformers for time series forecasting with exogenous variables. arXiv preprint arXiv:2402.19072 (2024)."},{"key":"e_1_3_2_2_43_1","unstructured":"Andrew Robert Williams Arjun Ashok \u00c9tienne Marcotte Valentina Zantedeschi Jithendaraa Subramanian Roland Riachi James Requeima Alexandre Lacoste Irina Rish Nicolas Chapados et al. 2024. Context is key: A benchmark for forecasting with essential textual information. arXiv preprint arXiv:2410.18959 (2024)."},{"key":"e_1_3_2_2_44_1","volume-title":"International Conference on Learning Representations.","author":"Wu Haixu","year":"2023","unstructured":"Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, and Mingsheng Long. 2023. TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_45_1","volume-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in neural information processing systems","author":"Wu Haixu","year":"2021","unstructured":"Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in neural information processing systems, Vol. 34 (2021), 22419-22430."},{"key":"e_1_3_2_2_46_1","volume-title":"Number Cookbook: Number Understanding of Language Models and How to Improve It. arXiv preprint arXiv:2411.03766","author":"Yang Haotong","year":"2024","unstructured":"Haotong Yang, Yi Hu, Shijia Kang, Zhouchen Lin, and Muhan Zhang. 2024. Number Cookbook: Number Understanding of Language Models and How to Improve It. arXiv preprint arXiv:2411.03766 (2024)."},{"key":"e_1_3_2_2_47_1","volume-title":"Are Transformers Effective for Time Series Forecasting? arXiv preprint arXiv:2205.13504","author":"Zeng Ailing","year":"2022","unstructured":"Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2022. Are Transformers Effective for Time Series Forecasting? arXiv preprint arXiv:2205.13504 (2022)."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"e_1_3_2_2_49_1","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."}],"event":{"name":"CIKM '25: The 34th ACM International Conference on Information and Knowledge Management","location":"Seoul Republic of Korea","acronym":"CIKM '25","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the 34th ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746252.3761278","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:27:33Z","timestamp":1765499253000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746252.3761278"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":49,"alternative-id":["10.1145\/3746252.3761278","10.1145\/3746252"],"URL":"https:\/\/doi.org\/10.1145\/3746252.3761278","relation":{},"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"2025-11-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}