Elamur Research⌕
☰
home / papers / 2025 / Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement

Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement

Paper · 2025 · Yaxuan Kong, Yiyuan Yang, Yoontae Hwang et al.

Summary

Most time series research targets one numerical task at a time, such as forecasting or anomaly detection. There is also little data that pairs natural language with time series, which holds back models able to discuss and reason about temporal data. The authors propose Time-MQA, a framework that treats several time series tasks as question answering. A user poses a natural-language question, adds optional context such as domain metadata, and the model answers using both the context and the raw series. To support this, they built TSQA, a corpus of about 200k question-answer pairs. It spans more than twelve domains and five task types, and its open-ended questions come with textual explanations. They continually pre-trained Mistral 7B, Llama-3 8B and Qwen-2.5 7B on TSQA using LoRA adapters. The tuned models produced reasoned answers, and in a user study they were often preferred over GPT-4o. The work matters because it links classical time series analysis with conversational LLM use, and the authors describe TSQA as the first large multi-domain, multi-task QA resource of its kind.

Key points

  • TSQA dataset: about 200k QA pairs drawn from domains such as healthcare, finance, energy, traffic, environment, AIOps, IoT, the web and human activity, with series of varying lengths.
  • Five tasks: four numerical tasks (predicting future values, filling in missing values, detecting anomalies, and assigning series to classes) plus question answering that calls for open-ended reasoning.
  • Models: Mistral 7B, Llama-3 8B and Qwen-2.5 7B were continually pre-trained with LoRA. The dataset, models and questionnaires are released on Hugging Face under the Time-MQA organization.
  • User study (78 participants) on accuracy and completeness:
    • Mistral was preferred for cyclical-pattern identification (80.8%).
    • Mistral was judged most comprehensive on volatility analysis (69.2%).
    • Qwen led on trend analysis, at 51.3% versus 37.2% for Mistral.
  • User study, overall preference: Mistral was chosen by 70.5% of participants. Llama and GPT-4o were less favored.
  • Limitations: the financial data covers only earnings call transcripts, and irregular series are poorly covered. Long numeric inputs carry a risk of hallucination, open-ended answers are hard to evaluate automatically, and fine-tuning is computationally costly.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of OxfordOxford · United Kingdom104 works · 157,183 citations🎓The University of Texas at AustinAustin · United States109 works · 56,942 citations🎓Griffith UniversityBrisbane · Australia10 works · 4,223 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 worksSignal Processing · 452 works

🧭 Topics 1Research subjects and languages the work is about.Question answering · 441 works

Cite

Cite as Yaxuan Kong, Yiyuan Yang, Yoontae Hwang, Wenjie Du, Stefan Zohren, Zhangyang Wang, Ming Jin and Qingsong Wen. 2025. Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.acl-long.1437

@inproceedings{kong2025time,
  title = {{Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement}},
  author = {Yaxuan Kong, Yiyuan Yang, Yoontae Hwang, Wenjie Du, Stefan Zohren, Zhangyang Wang, Ming Jin and Qingsong Wen},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.1437},
  url = {https://doi.org/10.18653/v1/2025.acl-long.1437}
}

References

79 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. 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.
  2. Subutai Ahmad, Alexander Lavin, Scott Purdy, and Zuha Agha. 2017. Unsupervised real-time anomaly detection for streaming data. Neurocomputing, 262:134–147. find
  3. Abdul Fatir Ansari, Lorenzo Stella, 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. arXiv preprint arXiv:2403.07815. — [arXiv:2403.07815](https://arxiv.org/abs/2403.07815) find
  4. Marc Bachlin, Daniel Roggen, Gerhard Troster, Meir Plotnik, Noit Inbar, Inbal Meidan, Talia Herman, Marina Brozgol, Eliya Shaviv, Nir Giladi, et al. 2009. Potentials of enhanced context awareness in wearable assistants for parkinson’s disease patients with the freezing of gait syndrome. In 2009 Inte… find
  5. Guillermo Barrenetxea. 2019. Sensorscope data. find
  6. Michele Bernardini, Anastasiia Doinychko, Luca Romeo, Emanuele Frontoni, and Massih-Reza Amini. 2023. A novel missing data imputation approach based on clinical conditional generative adversarial networks applied to ehr datasets. Computers in Biology and Medicine, 163:107188. find
  7. Giovanni Maria Biancofiore, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, and Fedelucio Narducci. 2024. Interactive question answering systems: Literature review. ACM Computing Surveys, 56(9):1–38. find
  8. Jian Chen, Peilin Zhou, Yining Hua, Yingxin Loh, Kehui Chen, Ziyuan Li, Bing Zhu, and Junwei Liang. 2024a. Fintextqa: A dataset for long-form financial question answering. Association for Computational Linguistics(ACL). find
  9. Wei Chen, Yuxuan Liang, Yuanshao Zhu, Yanchuan Chang, Kang Luo, Haomin Wen, Lei Li, Yanwei Yu, Qingsong Wen, Chao Chen, et al. 2024b. Deep learning for trajectory data management and mining: A survey and beyond. arXiv preprint arXiv:2403.14151. — [arXiv:2403.14151](https://arxiv.org/abs/2403.14151) find
  10. Xiaoyang Chen, Ben He, Hongyu Lin, Xianpei Han, Tianshu Wang, Boxi Cao, Le Sun, and Yingfei Sun. 2024c. Spiral of silences: How is large language model killing information retrieval?–a case study on open domain question answering. Association for Computational Linguistics(ACL). find
  11. Zhuo Chen, Wen Zhang, Yufeng Huang, Mingyang Chen, Yuxia Geng, Hongtao Yu, Zhen Bi, Yichi Zhang, Zhen Yao, Wenting Song, et al. 2023. Tele-knowledge pre-training for fault analysis. In 2023 IEEE 39th International Conference on Data Engineering (ICDE), pages 3453–3466. IEEE. find
  12. Daixuan Cheng, Yuxian Gu, Shaohan Huang, Junyu Bi, Minlie Huang, and Furu Wei. 2024. Instruction pre-training: Language models are supervised multitask learners. arXiv preprint arXiv:2406.14491.
  13. Winnie Chow, Lauren Gardiner, Haraldur T Hallgríms-son, Maxwell A Xu, and Shirley You Ren. 2024. Towards time series reasoning with llms. arXiv preprint arXiv:2409.11376. — [arXiv:2409.11376](https://arxiv.org/abs/2409.11376) find
  14. Hoang Anh Dau, Eamonn Keogh, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, and Chotirat Ann Ratanamahatana. 2018. Yanping, bing hu, nurjahan begum, anthony bagnall, abdullah mueen, gustavo batista, and hexagon-ml. the ucr time series classification archive, october 2018. find
  15. Wenjie Du, Jun Wang, Linglong Qian, Yiyuan Yang, Zina Ibrahim, Fanxing Liu, Zepu Wang, Haoxin Liu, Zhiyuan Zhao, Yingjie Zhou, et al. 2024. Tsi-bench: Benchmarking time series imputation. arXiv preprint arXiv:2406.12747. — [arXiv:2406.12747](https://arxiv.org/abs/2406.12747) find
  16. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783.
  17. Nigel Fernandez, Alexander Scarlatos, and Andrew Lan. 2024. Syllabusqa: A course logistics question answering dataset. arXiv preprint arXiv:2403.14666. find
  18. Elizabeth Fons, Rachneet Kaur, Soham Palande, Zhen Zeng, Tucker Balch, Manuela Veloso, and Svitlana Vyetrenko. 2024. Evaluating large language models on time series feature understanding: A comprehensive taxonomy and benchmark. arXiv preprint arXiv:2404.16563. find
  19. Giacomo Frisoni, Alessio Cocchieri, Alex Presepi, Gianluca Moro, and Zaiqiao Meng. 2024. To generate or to retrieve? on the effectiveness of artificial contexts for medical open-domain question answering. Association for Computational Linguistics(ACL). find
  20. Wayne A Fuller. 2009. Introduction to statistical time series. John Wiley & Sons. find
  21. Yizhen Guo, Tian Zhou, Wanyi Jiang, Bo Wu, Liang Sun, and Rong Jin. 2024. Maximizing the impact of deep learning on subseasonal-to-seasonal climate forecasting: The essential role of optimization. arXiv preprint arXiv:2411.16728. — [arXiv:2411.16728](https://arxiv.org/abs/2411.16728) find
  22. James D Hamilton. 2020. Time series analysis. Princeton university press. find
  23. Songqiao Han, Xiyang Hu, Hailiang Huang, Minqi Jiang, and Yue Zhao. 2022. Adbench: Anomaly detection benchmark. Advances in Neural Information Processing Systems, 35:32142–32159. find
  24. Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. 2020. Constructing a multi-hop qa dataset for comprehensive evaluation of reasoning steps. arXiv preprint arXiv:2011.01060.
  25. Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, and Frank Hutter. 2025. Accurate predictions on small data with a tabular foundation model. Nature, 637(8045):319–326. find
  26. International AIOPS Challenges. 2018. Kpi anomaly detection. https://competition.aiops-challenge.com/home/competition/ 1484452272200032281. ↗
  27. Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023. Mistral 7b. arXiv preprint arXiv:2310.06825. find
  28. Chuanyang Jin, Yutong Wu, Jing Cao, Jiannan Xiang, Yen-Ling Kuo, Zhiting Hu, Tomer Ullman, Antonio Torralba, Joshua B Tenenbaum, and Tianmin Shu. 2024a. Mmtom-qa: Multimodal theory of mind question answering. arXiv preprint arXiv:2401.08743.
  29. 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. 2023a. Time-llm: Time series forecasting by reprogramming large language models. arXiv preprint arXiv:2310.01728. find
  30. Ming Jin, Qingsong Wen, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, Xiaoli Li, et al. 2023b. Large models for time series and spatio-temporal data: A survey and outlook. arXiv preprint arXiv:2310.10196. — [arXiv:2310.10196](https://arxiv.org/abs/2310.10196) find
  31. Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, and Qingsong Wen. 2024b. Position: What can large language models tell us about time series analysis. In Forty-first International Conference on Machine Learning. find
  32. Yaxuan Kong, Yiyuan Yang, Shiyu Wang, Chenghao Liu, Yuxuan Liang, Ming Jin, Stefan Zohren, Dan Pei, Yan Liu, and Qingsong Wen. 2025. Position: Empowering time series reasoning with multimodal llms. arXiv preprint arXiv:2502.01477. — [arXiv:2502.01477](https://arxiv.org/abs/2502.01477) find
  33. Jennifer R Kwapisz, Gary M Weiss, and Samuel A Moore. 2011. Activity recognition using cell phone accelerometers. ACM SigKDD Explorations Newsletter, 12(2):74–82. find
  34. Wing Lian, Bleys Goodson, Eugene Pentland, Austin Cook, Chanvichet Vong, and "Teknium". 2023. Openorca: An open dataset of gpt augmented flan reasoning traces. https://https://huggingface.co/Open-Orca/OpenOrca. find
  35. Bryan Lim and Stefan Zohren. 2021. Time-series forecasting with deep learning: a survey. Philosophical Transactions of the Royal Society A, 379(2194):20200209. find
  36. Haoxin Liu, Chenghao Liu, and B Aditya Prakash. 2024a. A picture is worth a thousand numbers: Enabling llms reason about time series via visualization. NAACL. find
  37. Haoxin Liu, Shangqing Xu, Zhiyuan Zhao, Lingkai Kong, Harshavardhan Kamarthi, Aditya B Sasanur, Megha Sharma, Jiaming Cui, Qingsong Wen, Chao Zhang, et al. 2024b. Time-mmd: Multi-domain multimodal dataset for time series analysis. In The Thirty-eight Conference on Neural Information Processing Syste… find
  38. Haoxin Liu, Zhiyuan Zhao, Jindong Wang, Harshavardhan Kamarthi, and B. Aditya Prakash. 2024c. LST-Prompt: Large language models as zero-shot time series forecasters by long-short-term prompting. In Findings of the Association for Computational Linguistics: ACL 2024, pages 7832–7840. Association for… find
  39. Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang, Yuanyuan Yang, Jiaming Tian, Hao He, Antong Li, Mengshen He, Zhengliang Liu, et al. 2023. Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, page 100017.
  40. Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, and Mingsheng Long. 2024d. Timer: Generative pre-trained transformers are large time series models. arXiv preprint arXiv:2402.02368. — [arXiv:2402.02368](https://arxiv.org/abs/2402.02368) find
  41. Ziqing Ma, Wenwei Wang, Tian Zhou, Chao Chen, Bingqing Peng, Liang Sun, and Rong Jin. 2024. Fu-sionsf: Fuse heterogeneous modalities in a vector quantized framework for robust solar power forecasting. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 5532… find
  42. Mike A Merrill, Mingtian Tan, Vinayak Gupta, Tom Hartvigsen, and Tim Althoff. 2024. Language models still struggle to zero-shot reason about time series. arXiv preprint arXiv:2404.11757. find
  43. John A Miller, Mohammed Aldosari, Farah Saeed, Nasid Habib Barna, Subas Rana, I Budak Arpinar, and Ninghao Liu. 2024. A survey of deep learning and foundation models for time series forecasting. arXiv preprint arXiv:2401.13912. — [arXiv:2401.13912](https://arxiv.org/abs/2401.13912) find
  44. Navid Mohammadi Foumani, Lynn Miller, Chang Wei Tan, Geoffrey I Webb, Germain Forestier, and Mahsa Salehi. 2024. Deep learning for time series classification and extrinsic regression: A current survey. ACM Computing Surveys, 56(9):1–45. find
  45. George B Moody and Roger G Mark. 2001. The impact of the mit-bih arrhythmia database. IEEE engineering in medicine and biology magazine, 20(3):45–50. find
  46. In Proceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pages 169–173. find
  47. Yuqi Nie, Yaxuan Kong, Xiaowen Dong, John M Mulvey, H Vincent Poor, Qingsong Wen, and Stefan Zohren. 2024. A survey of large language models for financial applications: Progress, prospects and challenges. arXiv preprint arXiv:2406.11903. — [arXiv:2406.11903](https://arxiv.org/abs/2406.11903) find
  48. Aditi Singh, Abul Ehtesham, Saket Kumar, and Tala Talaei Khoei. 2025. Agentic retrieval-augmented generation: A survey on agentic rag. arXiv preprint arXiv:2501.09136. — [arXiv:2501.09136](https://arxiv.org/abs/2501.09136) find
  49. Kuldeep Singh, Simerjot Kaur, and Charese Smiley. 2024. Finqapt: Empowering financial decisions with end-to-end llm-driven question answering pipeline. In Proceedings of the 5th ACM International Conference on AI in Finance, pages 266–273. find
  50. The Motley Fool. 2024. Earnings call transcripts. find
  51. Markus Thill, Wolfgang Konen, and Thomas Bäck. 2020. Markusthill/mgab: the mackey-glass anomaly benchmark. Version v1. 0.1. Zenodo. doi, 10. find
  52. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
  53. Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022. Musique: Multi-hop questions via single-hop question composition. Transactions of the Association for Computational Linguistics, 10:539–554. find
  54. Volcengine. 2023. Doubao. find
  55. Chengsen Wang, Qi Qi, Jingyu Wang, Haifeng Sun, Zirui Zhuang, Jinming Wu, Lei Zhang, and Jianxin Liao. 2024a. Chattime: A unified multimodal time series foundation model bridging numerical and textual data. Preprint, arXiv:2412.11376. — [arXiv:2412.11376](https://arxiv.org/abs/2412.11376) find
  56. Jun Wang, Wenjie Du, Yiyuan Yang, Linglong Qian, Wei Cao, Keli Zhang, Wenjia Wang, Yuxuan Liang, and Qingsong Wen. 2025. Deep learning for multivariate time series imputation: A survey. Preprint, arXiv:2402.04059. — [arXiv:2402.04059](https://arxiv.org/abs/2402.04059) find
  57. Michael Moor, Oishi Banerjee, Zahra Shakeri Hossein Abad, Harlan M Krumholz, Jure Leskovec, Eric J Topol, and Pranav Rajpurkar. 2023. Foundation models for generalist medical artificial intelligence. Nature, 616(7956):259–265. find
  58. Yuxuan Wang, Haixu Wu, Jiaxiang Dong, Guo Qin, Haoran Zhang, Yong Liu, Yunzhong Qiu, Jianmin Wang, and Mingsheng Long. 2024b. Timexer: Empowering transformers for time series forecasting with exogenous variables. arXiv preprint arXiv:2402.19072. find
  59. Ozan Baris Mulayim, Pengrui Quan, Liying Han, Xiaomin Ouyang, Dezhi Hong, Mario Bergés, and Mani Srivastava. 2024. Are time series foundation models ready to revolutionize predictive building analytics? find
  60. Qingsong Wen, Liang Sun, Fan Yang, Xiaomin Song, Jingkun Gao, Xue Wang, and Huan Xu. 2020. Time series data augmentation for deep learning: A survey. arXiv preprint arXiv:2002.12478. — [arXiv:2002.12478](https://arxiv.org/abs/2002.12478) find
  61. Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun. 2022. Transformers in time series: A survey. arXiv preprint arXiv:2202.07125. — [arXiv:2202.07125](https://arxiv.org/abs/2202.07125) find
  62. Andrew Robert Williams, Arjun Ashok, Étienne 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:241… — [arXiv:2410.18959](https://arxiv.org/abs/2410.18959) find
  63. Zhe Xie, Zeyan Li, Xiao He, Longlong Xu, Xidao Wen, Tieying Zhang, Jianjun Chen, Rui Shi, and Dan Pei. 2024. Chatts: Aligning time series with llms via synthetic data for enhanced understanding and reasoning. arXiv preprint arXiv:2412.03104. — [arXiv:2412.03104](https://arxiv.org/abs/2412.03104) find
  64. Jingwen Xu, Fei Lyu, and Pong C Yuen. 2023. Density-aware temporal attentive step-wise diffusion model for medical time series imputation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pages 2836–2845. find
  65. Zhijian Xu, Yuxuan Bian, Jianyuan Zhong, Xiangyu Wen, and Qiang Xu. 2024. Beyond trend and periodicity: Guiding time series forecasting with textual cues. arXiv preprint arXiv:2405.13522. — [arXiv:2405.13522](https://arxiv.org/abs/2405.13522) find
  66. Yahoo Inc. 2021. S5-a labeled anomaly detection dataset, version 1.0. https://webscope.sandbox.yahoo.com/catalog.php?datatype=s&did=70. ↗
  67. An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. 2024. Qwen2. 5 technical report. arXiv preprint arXiv:2412.15115. find
  68. Yiyuan Yang, Rongshang Li, Qiquan Shi, Xijun Li, Gang Hu, Xing Li, and Mingxuan Yuan. 2023a. Sgdp: A stream-graph neural network based data prefetcher. In 2023 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE. find
  69. Yiyuan Yang, Yi Li, Taojia Zhang, Yan Zhou, and Haifeng Zhang. 2021a. Early safety warnings for long-distance pipelines: A distributed optical fiber sensor machine learning approach. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 14991–14999. find
  70. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018. Hotpotqa: A dataset for diverse, explainable multi-hop question answering. arXiv preprint arXiv:1809.09600.
  71. Jia-Yu Yao, Kun-Peng Ning, Zhen-Hui Liu, Mu-Nan Ning, Yu-Yang Liu, and Li Yuan. 2023. Llm lies: Hallucinations are not bugs, but features as adversarial examples. arXiv preprint arXiv:2310.01469. — [arXiv:2310.01469](https://arxiv.org/abs/2310.01469) find
  72. Zahra Zamanzadeh Darban, Geoffrey I Webb, Shirui Pan, Charu Aggarwal, and Mahsa Salehi. 2024. Deep learning for time series anomaly detection: A survey. ACM Computing Surveys, 57(1):1–42. find
  73. Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Y Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, et al. 2024a. Self-supervised learning for time series analysis: Taxonomy, progress, and prospects. IEEE Transactions on Pattern Analysis and Machine Intelligence. find
  74. Weiqi Zhang, Jiexia Ye, Ziyue Li, Jia Li, and Fugee Tsung. 2024b. Dualtime: A dual-adapter multimodal language model for time series representation. arXiv preprint arXiv:2406.06620. — [arXiv:2406.06620](https://arxiv.org/abs/2406.06620) find
  75. Yunkai Zhang, Yawen Zhang, Ming Zheng, Kezhen Chen, Chongyang Gao, Ruian Ge, Siyuan Teng, Amine Jelloul, Jinmeng Rao, Xiaoyuan Guo, Chiang-Wei Fang, Zeyu Zheng, and Jie Yang. 2023. Insight miner: A large-scale multimodal model for insight mining from time series. In NeurIPS 2023 AI for Science Works… find
  76. Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 11106–11115. find
  77. Yuchen Zhuang, Yue Yu, Kuan Wang, Haotian Sun, and Chao Zhang. 2024. Toolqa: A dataset for llm question answering with external tools. Advances in Neural Information Processing Systems, 36.
  78. Yiyuan Yang, Chaoli Zhang, Tian Zhou, Qingsong Wen, and Liang Sun. 2023b. Dcdetector: Dual attention contrastive representation learning for time series anomaly detection. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 3033–3045. find
  79. Yiyuan Yang, Haifeng Zhang, and Yi Li. 2021b. Long-distance pipeline safety early warning: a distributed optical fiber sensing semi-supervised learning method. IEEE sensors journal, 21(17):19453– 19461. find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/kong-2025-time-mqa-time-series-multi/