Computer Science ›› 2026, Vol. 53 ›› Issue (9): 55-70.doi: 10.11896/jsjkx.250800082

• Research and Application of Large Language Model Technology • Previous Articles     Next Articles

Review of Large Language Model-based Time Series Modeling via Fine-tuning and AgentArchitecture

ZHANG Rongjie1, PANG Xiongwen1,2, WANG Fengling2   

  1. 1 School of Computer Science,South China Normal University,Guangzhou 510631,China
    2 School of Artificial Intelligence,South China Normal University,Foshan,Guangdong 528225,China
  • Received:2025-08-18 Revised:2026-02-24 Online:2026-09-15 Published:2026-09-10
  • About author:ZHANG Rongjie,born in 2001,postgraduate.His main research interests include time series analysis and LLMs fine-tuning.
    PANG Xiongwen,born in 1972,Ph.D,associate professor.His main research interests include artificial intelligence and data mining.

Abstract: Large language models(LLMs) show broad prospects for applications in time series analysis across domains such as climate,IoT,healthcare,and finance,they can capture temporal dependencies and latent patterns in sequential data,thereby enhancing forecasting,diagnosis,and decision-making tasks.However,systematic reviews in this direction-particularly those focusing on agent architectures and cross-modal adaptation-remain limited.Accordingly,this paper systematically reviews recent advances in applying LLMs to time series modeling,categorizing existing approaches into three types:prompt-based tuning,vector encoding tuning,and agent architectures integrated with external tool libraries.Furthermore,it provides a domain-wise overview of representative datasets and evaluation metrics.Finally,this paper highlights key challenges-including cross-modal fusion,interpre-tability,and model generalization—and discusses potential research directions,aiming to provide constructive insights for future technological innovation and real-world deployment of LLMs in time series analysis.

Key words: Time series analysis, Large language models, Fine-tuning, Agent architectures, Cross-modal

CLC Number: 

  • TP389
[1] JORDAN M I.Chapter 25-Serial order:A parallel distributed processing approach[J].Advances in Psychology,1997,121:471-495.
[2] GRAVES A.Long short-term memory[M] //Supervised Sequence Labelling with Recurrent Neural Networks.2012:37-45.
[3] VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[J].Advances in Neural Information Processing Systems,2017,30:5998-6008.
[4] WU H,XU J,WANG J,et al.Autoformer:Decomposition transformers with auto-correlation for long-term series forecasting[J].Advances in Neural Information Processing Systems,2021,34:22419-22430.
[5] ZHOU H,ZHANG S,PENG J,et al.Informer:Beyond efficient transformer for long sequence time-series forecasting[C] //Proceedings of the AAAI Conference on Artificial Intelligence.2021:11106-11115.
[6] LIU Y,HU T,ZHANG H,et al.iTransformer:Inverted Transformers Are Effective for Time Series Forecasting[C] //The Twelfth International Conference on Learning Representations.2024.
[7] XIAO J L,QIU X,ZHANG Y,et al.Review on large language models in transportation[J].Journal of Traffic and Transportation Engineering,2025,25(1):8-28.
[8] YIN S,FU C,ZHAO S,et al.A survey on multimodal large language models[J].National Science Review,2024,11(12):nwae403.
[9] CHANG Y,WANG X,WANG J,et al.A survey on evaluation of large language models[J].ACM Transactions on Intelligent Systems and Technology,2024,15(3):1-45.
[10] ANSARI A F,STELLA L,TURKMEN A C,et al.Chronos:Learning the Language of Time Series[J].arXiv:2403.07815,2024.
[11] RONG Y,BIAN Y,XU T,et al.Self-supervised graph trans-former on large-scale molecular data[J].Advances in Neural Information Processing Systems,2020,33:12559-12571.
[12] DWIVEDI V P,BRESSON X.A generalization of transformer networks to graphs[J].arXiv:2012.09699,2020.
[13] YING C,CAI T,LUO S,et al.Do transformers really perform badly for graph representation?[J].Advances in Neural Information Processing Systems,2021,34:28877-28888.
[14] YUN S,JEONG M,KIM R,et al.Graph transformer networks[C] //Advances in Neural Information Processing Systems.2019.
[15] IMANI S,DU L,SHRIVASTAVA H.MathPrompter:Mathematical Reasoning using Large Language Models[C] //ICLR 2023 Workshop on Trustworthy and Reliable Large-Scale Machine Learning Models.2023.
[16] LEWKOWYCZ A,ANDREASSEN A,DOHAN D,et al.Solving quantitative reasoning problems with language models[J].Advances in Neural Information Processing Systems,2022,35:3843-3857.
[17] WEI J,WANG X,SCHUURMANS D,et al.Chain-of-thoughtprompting elicits reasoning in large language models[J].Advances in Neural Information Processing Systems,2022,35:24824-24837.
[18] XUE H,SALIM F D.Promptcast:A new prompt-based learning paradigm for time series forecasting[J].IEEE Transactions on Knowledge and Data Engineering,2023,36(11):6851-6864.
[19] LI Z,ZHAO N,ZHANG S,et al.Constructing large-scale real-world benchmark datasets for aiops[J].arXiv:2208.03938,2022.
[20] XU X,WANG H,LIANG Y,et al.Can Multimodal LLMs Perform Time Series Anomaly Detection?[J].arXiv:2502.17812,2025.
[21] HE K,MAO R,LIN Q,et al.A survey of large language modelsfor healthcare:from data,technology,and applications to accountability and ethics[J].Information Fusion,2025,118:102963.
[22] SU J,JIANG C,JIN X,et al.Large language models for forecasting and anomaly detection:A systematic literature review[J].arXiv:2402.10350,2024.
[23] GRUVER N,FINZI M,QIU S,et al.Large language models are zero-shot time series forecasters[J].Advances in Neural Information Processing Systems,2023,36:19622-19635.
[24] LI Y,WANG S,DING H,et al.Large language models in fi-nance:A survey[C] //Proceedings of the Fourth ACM International Conference on AI in Finance.2023:374-382.
[25] KIM J,KIM H,KIM H G,et al.A comprehensive survey of deep learning for time series forecasting:architectural diversity and open challenges[J].Artificial Intelligence Review,2025,58(7):1-95.
[26] JIN M,KOH H Y,WEN Q,et al.A survey on graph neural networks for time series:Forecasting,classification,imputation,and anomaly detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2024,46(12):10466-10485.
[27] CHE W X,DOU Z C,FENG Y S,et al.Towards a comprehensive understanding of the impact of large language models on natural language processing:challenges,opportunities and future directions [J].Scientia Sinical Informations,2023,53:1645-1687.
[28] LI C T,HAN X,JIANG Ruo H,et al.Application and prospects of large models in materials science[J].Chinese Journal of Engineering,2024,46:290-305.
[29] LEE G,YU W,SHIN K,et al.Timecap:Learning to contextua-lize,augment,and predict time series events with large language model agents[C] //Proceedings of the AAAI Conference on Artificial Intelligence.2025:18082-18090.
[30] ZHAO Z,WANG P,WEN H,et al.STEM-LTS:Integrating Semantic-Temporal Dynamics in LLM-driven Time Series Analysis[C] //Proceedings of the AAAI Conference on Artificial Intelligence.2025:22858-22866.
[31] RADFORD A,WU J,CHILD R,et al.Language models are unsupervised multitask learners[J].OpenAI Blog,2019,1(8):9.
[32] DEVLIN J,CHANG M W,LEE K,et al.Bert:Pre-training of deep bidirectional transformers for language understanding[C] //Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2019:4171-4186.
[33] ZHOU C,LI Q,LI C,et al.A comprehensive survey on pretrained foundation models:A history from bert to chatgpt[J].International Journal of Machine Learning and Cybernetics,2025,16:9851-9915.
[34] MOHANASUNDARAM V,RANGASWAMY B.Photovoltaic solar energy prediction using the seasonal-trend decomposition layer and ASOA optimized LSTM neural network model[J].Scientific Reports,2025,15(1):4032.
[35] LI X,ZHANG W,LI X,et al.Partial domain adaptation in remaining useful life prediction with incomplete target data[J].IEEE/ASME Transactions on Mechatronics,2023,29(3):1903-1913.
[36] GOSWAMI M,SZAFER K,CHOUDHRY A,et al.MOMENT:A Family of Open Time-series Foundation Models[C] //Forty-first International Conference on Machine Learning.2024.
[37] DAS A,KONG W,SEN R,et al.A decoder-only foundationmodel for time-series forecasting[C] //Forty-first International Conference on Machine Learning.2024.
[38] LIU Y,ZHANG H,LI C,et al.Timer:Generative Pre-trainedTransformers Are Large Time Series Models[C] //International Conference on Machine Learning.PMLR,2024:32369-32399.
[39] GARZA A,CHALLU C,MERGENTHALER-CANSECO M.TimeGPT-1[J].arXiv:2310.03589,2023.
[40] SUN C,LI H,LI Y,et al.TEST:Text Prototype Aligned Embedding to Activate LLM’s Ability for Time Series[C] //The Twelfth International Conference on Learning Representations.2024.
[41] LIU P,YUAN W,FU J,et al.Pre-train,prompt,and predict:A systematic survey of prompting methods in natural language processing[J].ACM Computing Surveys,2023,55(9):1-35.
[42] LI Y.A Practical Survey on Zero-Shot Prompt Design for In-Context Learning[C] //Proceedings of the 14th International Conference on Recent Advances in Natural Language Proces-sing.2023:641-647.
[43] YU X,CHEN Z,LU Y.Harnessing LLMs for Temporal Data-A Study on Explainable Financial Time Series Forecasting[C] //Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing:Industry Track.2023:739-753.
[44] ZHANG B,YANG H,LIU X Y.Instruct-FinGPT:FinancialSentiment Analysis by Instruction Tuning of General-Purpose Large Language Models[J].arXiv:2306.12659,2023.
[45] ENGLHARDT Z,MA C,MORRIS M E,et al.From classification to clinical insights:Towards analyzing and reasoning about mobile and behavioral health data with large language models[C] //Proceedings of the ACM on Interactive,Mobile,Wearable and Ubiquitous Technologies.2024:1-25.
[46] YU X,CHEN Z,LING Y,et al.Temporal data meets LLM-explainable financial time series forecasting[J].arXiv:2306.11025,2023.
[47] MIRCHANDANI S,XIA F,FLORENCE P,et al.Large Lan-guage Models as General Pattern Machines[C] //Conference on Robot Learning.PMLR,2023:2498-2518.
[48] JIN M,WANG S,MA L,et al.Time-LLM:Time Series Forecasting by Reprogramming Large Language Models[C] //International Conference on Learning Representations.2024.
[49] PAN Z,JIANG Y,GARG S,et al.$ s∧ 2$ IP-LLM:Semantic space informed prompt learning with LLM for time series forecasting[C] //Forty-first International Conference on Machine Learning.2024.
[50] JIA F,WANG K,ZHENG Y,et al.Gpt4mts:Prompt-basedlarge language model for multimodal time-series forecasting[C] //Proceedings of the AAAI Conference on Artificial Intelligence.2024:23343-23351.
[51] LIU X,HU J,LI Y,et al.Unitime:A language-empowered unified model for cross-domain time series forecasting[C] //Proceedings of the ACM Web Conference 2024.2024:4095-4106.
[52] CAO D,JIA F,ARIK S O,et al.TEMPO:Prompt-based Generative Pre-trained Transformer for Time Series Forecasting[C] //The Twelfth International Conference on Learning Representations.2024.
[53] LIU P,GUO H,DAI T,et al.Calf:Aligning llms for time series forecasting via cross-modal fine-tuning[C] //Proceedings of the AAAI Conference on Artificial Intelligence.2025:18915-18923.
[54] LIU Q,LIU X,LIU C,et al.Time-ffm:Towards lm-empowered federated foundation model for time series forecasting[J].Advances in Neural Information Processing Systems,2024,37:94512-94538.
[55] CHEN C,OLIVEIRA G,NOGHABI H S,et al.LLM-TS Integrator:Integrating LLM for Enhanced Time Series Modeling[J].arXiv:2410.16489,2024.
[56] HU Y,LI Q,ZHANG D,et al.Context-Alignment:Activatingand Enhancing LLMs Capabilities in Time Series[C] //The Thirteenth International Conference on Learning Representations.2025.
[57] XUE H,VOUTHAROJA B P,SALIM F D.Leveraging language foundation models for human mobility forecasting[C] //Proceedings of the 30th International Conference on Advances in Geographic Information Systems.2022:1-9.
[58] LI J,LIU C,CHENG S,et al.Frozen language model helps ecg zero-shot learning[C] //Medical Imaging with Deep Learning.PMLR,2024:402-415.
[59] CHEN L,ZHONG X,LI H,et al.A machine learning model that outperforms conventional global subseasonal forecast mo-dels[J].Nature Communications,2024,15(1):6425.
[60] HU E J,WALLIS P,ALLEN ZHU Z,et al.LoRA:Low-Rank Adaptation of Large Language Models[C] //International Conference on Learning Representations.2022.
[61] MA T,ZHAO Y,LI M,et al.TPLLM:A traffic predictionframework based on pretrained Large Language Models[J].Applied Soft Computing,2026,184:113840.
[62] ZHANG J,GAO J,OUYANG W,et al.Time-LlaMA:Adapting Large Language Models for Time Series Modeling via Dynamic Low-rank Adaptation[C] //Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics.2025:1145-1157.
[63] LIU L,YU S,WANG R,et al.How can large language models understand spatial-temporal data?[J].arXiv:2401.14192,2024.
[64] LIU R,LI C,TANG H,et al.St-llm:Large language models are effective temporal learners[C] //European Conference on Computer Vision.Cham:Springer,2024:1-18.
[65] PARMANTO B,ARYOYUDANTA B,SOEKINTO T W,et al.A reliable and accessible caregiving language model(CaLM) to support tools for caregivers:Development and evaluation study[J].JMIR Formative Research,2024,8:e54633.
[66] LENG Z,BHATTACHARJEE A,RAJASEKHAR H,et al.Imugpt 2.0:Language-based cross modality transfer for sensor-based human activity recognition[C] //Proceedings of the ACM on Interactive,Mobile,Wearable and Ubiquitous Technologies.2024:1-32.
[67] ZHANG J,ZHANG Y,CUN X,et al.Generating human motion from textual descriptions with discrete representations[C] //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2023:14730-14740.
[68] WIMMER C,REKABSAZ N.Leveraging vision-language mo-dels for granular market change prediction[J].arXiv:2301.10166,2023.
[69] WANG X,FENG M,QIU J,et al.From news to forecast:Integrating event analysis in llm-based time series forecasting with reflection[J].Advances in Neural Information Processing Systems,2024,37:58118-58153.
[70] CHENG J,CHIN P.Sociodojo:Building lifelong analyticalagents with real-world text and time series[C] //The Twelfth International Conference on Learning Representations.2024.
[71] DING Y,JIA S,MA T,et al.Integrating stock features and global information via large language models for enhanced stock return prediction[J].arXiv:2310.05627,2023.
[72] LIN C,LYU H,LUO J,et al.Harnessing gpt-4v(ision) for insurance:A preliminary exploration[J].arXiv:2404.09690,2024.
[73] WEI H,YANG Z,WANG Z.Aniportrait:Audio-driven synthesis of photorealistic portrait animation[J].arXiv:2403.17694,2024.
[74] ZHANG Q,XU C,LI J,et al.LLM-TSFD:An industrial time series human-in-the-loop fault diagnosis method based on a large language model[J].Expert Systems with Applications,2025,264:125861.
[75] SUN Z,ZHOU X,WU J,et al.D-Bot:An LLM-Powered DBA Copilot[C] //Companion of the 2025 International Conference on Management of Data.2025:235-238.
[76] GAO S,WEN Y,ZHU M,et al.Simulating financial market via large language model based agents[J].arXiv:2406.19966,2024.
[77] QIN Y,LIANG S,YE Y,et al.ToolLLM:Facilitating LargeLanguage Models to Master 16000+ Real-world APIs[C] //The Twelfth International Conference on Learning Representations.2024.
[78] AN S,LI Q,LU J,et al.Finverse:An autonomous agent system for versatile financial analysis[J].arXiv:2406.06379,2024.
[79] TU X,ZOU J,SU W,et al.What Should Data Science Education Do With Large Language Models?[J].Harvard Data Science Review,2024,6(1).
[80] CHENG L,LI X,BING L.Is GPT-4 a Good Data Analyst?[C] //The 2023 Conference on Empirical Methods in Natural Language Processing.2023:9496-9514.
[81] ÁLVARO J A H,BARREDA J G.An advanced retrieval-augmented generation system for manufacturing quality control[J].Advanced Engineering Informatics,2025,64:103007.
[82] WANG S,FAN Y,JIN S,et al.Improved anti-noise adaptivelong short-term memory neural network modeling for the robust remaining useful life prediction of lithiumion batteries[J].Reliability Engineering & System Safety,2023,230:108920.
[83] LIU P F,QIAN L,ZHAO X,et al.Joint knowledge graph and large language model for fault diagnosis and its application in aviation assembly[J].IEEE Transactions on Industrial Informa-tics,2024,20(6):8160-8169.
[84] TAO L,LIU H,NING G,et al.LLM-based framework for bea-ring fault diagnosis[J].Mechanical Systems and Signal Proces-sing,2025,224:112127.
[85] LIN L,ZHANG S,FU S,et al.FD-LLM:Large language model for fault diagnosis of complex equipment[J].Advanced Engineering Informatics,2025,65:103208.
[86] CHEN Y,XIE H,MA M,et al.Automatic root cause analysis via large language models for cloud incidents[C] //Proceedings of the Nineteenth European Conference on Computer Systems.2024:674-688.
[87] AHMED T,GHOSH S,BANSAL C,et al.Recommending root-cause and mitigation steps for cloud incidents using large language models[C] //2023 IEEE/ACM 45th International Confe-rence on Software Engineering(ICSE).IEEE,2023:1737-1749.
[88] TRIPATHI R,VERMA B.CLIP-LSTM:Fused Model for Dynamic Hand Gesture Recognition[C] //2023 IEEE 20th India Council International Conference(INDICON).IEEE,2023:926-931.
[89] RASUL K,ASHOK A,WILLIAMS A R,et al.Lag-llama:Towards foundation models for time series forecasting[C] //R0-FoMo:Robustness of Few-shot and Zero-shot Learning in Large Foundation Models.2023.
[90] WOO G,LIU C,KUMAR A,et al.Unified Training of Universal Time Series Forecasting Transformers[C] //International Conference on Machine Learning.PMLR,2024:53140-53164.
[91] ZHOU T,NIU P,SUN L,et al.One fits all:Power general time series analysis by pretrained lm[J].Advances in Neural Information Processing Systems,2023,36:43322-43355.
[92] LIU C,XU Q,MIAO H,et al.Timecma:Towards llm-empowered multivariate time series forecasting via cross-modality alignment[C] //Proceedings of the AAAI Conference on Artificial Intelligence.2025:18780-18788.
[93] YANG S,WANG D,ZHENG H,et al.Timerag:Boosting llm time series forecasting via retrieval-augmented generation[C] //ICASSP 2025-2025 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).IEEE,2025:1-5.
[94] JIANG Y,YU W,LEE G,et al.Explainable multi-modal timeseries prediction with llm-in-the-loop[J].arXiv:2503.01013,2025.
[95] ZHANG B,YANG H,ZHOU T,et al.Enhancing financial sentiment analysis via retrieval augmented large language models[C] //Proceedings of the Fourth ACM International Conference on AI in Finance.2023:349-356.
[96] XIE Q,HAN W,CHEN Z,et al.Finben:A holistic financial benchmark for large language models[J].Advances in Neural Information Processing Systems,2024,37:95716-95743.
[97] LEE J,YOON W,KIM S,et al.BioBERT:a pre-trained biomedical language representation model for biomedical text mining[J].Bioinformatics,2020,36(4):1234-1240.
[98] LI W,YU L,WU M,et al.DoctorGPT:A Large Language Mo-del with Chinese Medical Question-Answering Capabilities[C] //2023 International Conference on High Performance Big Data and Intelligent Systems(HDIS).IEEE,2023:186-193.
[99] LAKHDHAR W,ARABI M,IBRAHIM A,et al.ChatCVD:A Retrieval-Augmented Chatbot for Personalized Cardiovascular Risk Assessment with a Comparison of Medical-Specific and General-Purpose LLMs[J].AI,2025,6(8):163.
[100] WANG P,WEI X,HU F,et al.Transgpt:Multi-modal generative pre-trained transformer for transportation[C] //2024 international conference on computational linguistics and Natural Language processing(CLNLP).IEEE,2024:96-100.
[101] MASRI S,ASHQAR H I,ELHENAWY M.Leveraging large language models(LLMs) for traffic management at urban intersections:the case of mixed traffic scenarios[J].arXiv:2408.00948,2024.
[102] PRICE I,SANCHEZ-GONZALEZ A,ALET F,et al.Probabilistic weather forecasting with machine learning[J].Nature,2025,637(8044):84-90.
[103] LI S,YANG W,ZHANG P,et al.Climatellm:Efficient weather forecasting via frequency-aware large language models[J].ar-Xiv:2502.11059,2025.
[104] LIU X,MCDUFF D,KOVACS G I,et al.Large language models are few-shot health learners[J].arXiv:2305.15525,2023.
[105] WANG S,TAN J,DOU Z,et al.OmniEval:An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain[J].arXiv:2412.13018,2024.
[106] LU Q,DOU D,NGUYEN T.ClinicalT5:A generative language model for clinical text[C] //Findings of the Association for Computational Linguistics:EMNLP 2022.2022:5436-5443.
[107] ZENG A,LIU X,DU Z,et al.GLM-130B:An Open Bilingual Pre-trained Model[C] //The Eleventh International Conference on Learning Representations.2023.
[108] SAAB K,TU T,WENG W H,et al.Capabilities of gemini models in medicine[J].arXiv:2404.18416,2024.
[109] ZHANG S,FU D,LIANG W,et al.Trafficgpt:Viewing,processing and interacting with traffic foundation models[J].Transport Policy,2024,150:95-105.
[110] LIU C,YANG S,XU Q,et al.Spatial-temporal large language model for traffic prediction[C] //2024 25th IEEE International Conference on Mobile Data Management(MDM).IEEE,2024:31-40.
[111] CHENG H,GONG Z,WANG C.LLM-TFP:Integrating large language models with spatio-temporal features for urban traffic flow prediction[J].Applied Soft Computing,2025,177:113174.
[112] BI K,XIE L,ZHANG H,et al.Accurate medium-range global weather forecasting with 3D neural networks[J].Nature,2023,619(7970):533-538.
[113] YU Y M.Cornucopia-LLaMA-Fin-Chinese [EB/OL].https://github.com/jerry1993-tech/Cornucopia-LLaMA-Fin-Chinese.
[114] WANG N,YANG H,WANG C.FinGPT:Instruction TuningBenchmark for Open-Source Large Language Models in Financial Datasets[C] //NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following.2023.
[115] ZHANG X,YANG Q.Xuanyuan 2.0:A large chinese financial chat model with hundreds of billions parameters[C] //Procee-dings of the 32nd ACM International Conference on Information and Knowledge Management.2023:4435-4439.
[116] LIAO Y,JIANG S,WANG Y,et al.MING-MOE:Enhancingmedical multi-task learning in large language models with sparse mixture of low-rank adapter experts[J].arXiv:2404.09027,2024.
[117] XIE J,YU Y,CHEN Y,et al.BenCao:An Instruction-TunedLarge Language Model for Traditional Chinese Medicine[J].arXiv:2510.17415,2025.
[118] JIA Y,JI X,WANG X,et al.Qibo:A Large Language Model for traditional Chinese medicine[J].Expert Systems with Applications,2025:127672.
[119] XIE Q,HAN W,ZHANG X,et al.PIXIU:a large languagemodel,instruction data and evaluation benchmark for finance[C] //Proceedings of the 37th International Conference on Neural Information Processing Systems.2023:33469-33484.
[120] XU Y,COHEN S B.Stock movement prediction from tweets and historical prices[C] //Proceedings of the 56th Annual Mee-ting of the Association for Computational Linguistics.2018:1970-1979.
[121] QIN Y,SONG D,CHENG H,et al.A dual-stage attention-based recurrent neural network for time series prediction[C] //Proceedings of the 26th International Joint Conference on Artificial Intelligence.2017:2627-2633.
[122] BAO Z,CHEN W,XIAO S,et al.Disc-medllm:Bridging general large language models and real-world medical consultation[J].arXiv:2308.14346,2023.
[123] WAGNER P,STRODTHOFF N,BOUSSELJOT R D,et al.PTB-XL,a large publicly available electrocardiography dataset[J].Scientific Data,2020,7(1):1-15.
[124] OH J,LEE G,BAE S,et al.Ecg-qa:A comprehensive question answering dataset combined with electrocardiogram[J].Advances in Neural Information Processing Systems,2023,36:66277-66288.
[125] JOHNSON A E W,POLLARD T J,SHEN L,et al.MIMIC-III,a freely accessible critical care database[J].Scientific Data,2016,3(1):1-9.
[126] OLIVEIRA J,RENNA F,COSTA P D,et al.The CirCor DigiScope dataset:from murmur detection to murmur classification[J].IEEE Journal of Biomedical and Health Informatics,2021,26(6):2524-2535.
[127] LI Y,YU R,SHAHABI C,et al.Diffusion Convolutional Recurrent Neural Network:Data-Driven Traffic Forecasting[C] //International Conference on Learning Representations.2018.
[128] CHEN C,PETTY K,SKABARDONIS A,et al.Freeway performance measurement system:mining loop detector data[J].Transportation Research Record,2001,1748(1):96-102.
[129] XU L,HUANG H,LIU J.Sutd-trafficqa:A question answering benchmark and an efficient network for video reasoning over traffic events[C] //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:9878-9888.
[130] YAO H X,TANG X F,H.WEI H,et al.Revisiting spatialtemporal similarity:A deep learning framework for traffic prediction[C] //Proceedings of the AAAI Conference on Artificial Intelligence.2019:5668-5675.
[131] KLISE K,BEYELER W,FINLEY P,et al.Analysis of mobility data to build contact networks for COVID-19[J].PLoS One,2021,16(4):e0249726.
[132] LIU X,XIA Y,LIANG Y,et al.Largest:A benchmark dataset for large-scale traffic forecasting[J].Advances in Neural Information Processing Systems,2023,36:75354-75371.
[133] CHEN S,LONG G,SHEN T,et al.Spatial-temporal promptlearning for federated weather forecasting[J].arXiv:2305.14244,2023.
[134] RASP S,DUEBEN P D,SCHER S,et al.WeatherBench:A benchmark data set for datadriven weather forecasting[J].Journal of Advances in Modeling Earth Systems,2020,12:11.
[135] CHEN W,HAO X,WU Y,et al.Terra:A multimodal spatio-temporal dataset spanning the earth[J].Advances in Neural Information Processing Systems,2024,37:66329-66356.
[136] NATHANIEL J,QU Y,NGUYEN T,et al.Chaosbench:A multi-channel,physics-based benchmark for subseasonal-to-seasonal climate prediction[J].Advances in Neural Information Processing Systems,2024,37:43715-43729.
[137] CHICCO D,WARRENS M J,JURMAN G.The coefficient ofdetermination R-squared is more informative than SMAPE,MAE,MAPE,MSE and RMSE in regression analysis evaluation[J].PeerJ Computer Science,2021,7:e623.
[138] WU Y,WAN Y,CHU Z,et al.Can large language models serve as evaluators for code summarization?[J].IEEE Transactions on Software Engineering,2025,51(12):3205-3217.
[139] MAKRIDAKIS S,SPILIOTIS E,ASSIMAKOPOULOS V.The M4 Competition:Results,findings,conclusion and way forward[J].International Journal of Forecasting,2018,34(4):802-808.
[140] HEBRAIL G,BERARD A.Individual household electric power consumption data set[DB/OL].https://doi.org/10.24432/C58K54.
[141] CHENG Y,CHAI Z,ANWAR A.Characterizing co-located da-tacenter workloads:An alibaba case study[C] //Proceedings of the 9th Asia-Pacific Workshop on Systems.2018:1-3.
[142] DAU H A,BAGNALL A,KAMGAR K,et al.The UCR time series archive[J].IEEE/CAA Journal of Automatica Sinica,2019,6(6):1293-1305.
[143] LU D,WU H,LIANG J,et al.Bbt-fin:Comprehensive construction of chinese financial domain pre-trained language model,corpus and benchmark[J].arXiv:2302.09432,2023.
[144] GUO X,XIA H,LIU Z,et al.FinEval:A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models[C] //Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics:Human Language Technologies.2025:6258-6292.
[145] ZHU W,WANG X,ZHENG H,et al.Promptcblue:A chinese prompt tuning benchmark for the medical domain[J].arXiv:2310.14151,2023.
[146] JIN D,PAN E,OUFATTOLE N,et al.What disease does this patient have? a large-scale open domain question answering dataset from medical exams[J].Applied Sciences,2021,11(14):6421.
[147] PAL A,UMAPATHI L K,SANKARASUBBU M.Medmcqa:A large-scale multi-subject multi-choice dataset for medical domain question answering[C] //Conference on Health,Inference and Learning.PMLR,2022:248-260.
[148] HUANG Y,BAI Y,ZHU Z,et al.C-eval:A multi-level multi-discipline chinese evaluation suite for foundation models[J].Advances in Neural Information Processing Systems,2023,36:62991-63010.
[149] GU Z,ZHU X,YE H,et al.Xiezhi:An ever-updating bench-mark for holistic domain knowledge evaluation[C] //Procee-dings of the AAAI Conference on Artificial Intelligence.2024:18099-18107.
[150] QIN B,YUE C,YIN F,et al.FlagEval Findings Report:A Preliminary Evaluation of Large Reasoning Models on Automatically Verifiable Textual and Visual Questions[J].arXiv:2509.17177,2025.
[151] TAN M,MERRILL M,GUPTA V,et al.Are language models actually useful for time series forecasting?[J].Advances in Neural Information Processing Systems,2024,37:60162-60191.
[152] MA Q,LIU Z,ZHENG Z,et al.A survey on time-series pre-trained models[J].IEEE Transactions on Knowledge and Data Engineering,2024,36(12):7536-7555.
[153] ZHANG W,ZHAO L,XIA H,et al.trading:Tool-augmented,diversified,and generalist[C] //Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.2024:4314-4325.
[1] GUO Yuyang, SHI Lei, LIU Huan, DONG Yixiang, LI Rui. Domain-adapted and Dynamically Retrieval-augmented Approach for Large-scale History Discipline Model Construction [J]. Computer Science, 2026, 53(9): 92-100.
[2] WANG Xingyue, YE Hongting, XU Honghua, ZHOU Suyang, KONG Youyong. Multimodal Renewable Energy Data Feature Graph Modeling Method Based on Hard Prompts [J]. Computer Science, 2026, 53(9): 145-156.
[3] LI Zhennan, QIAN Jiayan, WANG Xinzhi, ZHANG Hui. Technology Risk Structure Recognition Based on Multi-granularity Semantic Dual Reflection [J]. Computer Science, 2026, 53(9): 395-404.
[4] HE Jiaojun, LI Xin. Review of Graph Learning Based on Large Language Models:Methods,Benchmarks and Advances [J]. Computer Science, 2026, 53(9): 1-15.
[5] LI Luozheng, LI Lingbo, YUAN Quan. Survey of Chinese Datasets for LLM Safety Alignment:Landscape and Prospects [J]. Computer Science, 2026, 53(9): 16-23.
[6] AN Ran, CHENG Kai, SHAO Tianhao, LI Changyuan, CHEN Yan. Framework,Techniques and Challenges of Task Planning Agent Based on Large Language Model [J]. Computer Science, 2026, 53(9): 24-37.
[7] LIU Jing. Review of Music Artificial Intelligence Driven by Large Language Models [J]. Computer Science, 2026, 53(8): 229-244.
[8] YANG Chenguang, LU Jicang, GUO Jiaxing. Fake News Detection Model Based on Cross-modal Feature Fusion and Alignment [J]. Computer Science, 2026, 53(8): 257-265.
[9] ZHANG Haoran, HAO Wenning, JIN Dawei, CHENG Kai, LIU Junyang. Agentic Retrieval Augmented Generation Framework Based on Retrieval Task Planning and Reflection Mechanism [J]. Computer Science, 2026, 53(8): 285-297.
[10] ZHANG Zhonglin, XIA Hang. LSQ-RAG:Retrieval-enhanced Generation Framework Based on LLM-enhanced Ranker [J]. Computer Science, 2026, 53(8): 298-306.
[11] ZHU Yuchao, ZHANG Shunxiang, WEN Boyu, SUN Liang, XU Yang. Frequency-augmented and Multi-level Feature Fusion for Image-Text Sentiment Analyzer [J]. Computer Science, 2026, 53(7): 71-79.
[12] CHEN Zhixiang, XIE Zhipeng. Event Causal Data Augmentation Method Based on Large Language Model [J]. Computer Science, 2026, 53(7): 125-131.
[13] ZHU Rong, HU Mengyao, DAI Lingyun, LI Feng. Single-cell Multi-omics Clustering Method Based on Missing Value Imputation and Cross-modalAlignment [J]. Computer Science, 2026, 53(7): 289-297.
[14] XU Rui, LIU Jin, LIU Xudong, GUAN Jian, DONG Wei. Exploring the Generalization Ability of Prompt-based Large Language Models for TextClassification [J]. Computer Science, 2026, 53(6A): 250400092-7.
[15] WEI Qing, ZHANG Yupeng, LIU Shaoxun, ZHANG Jinfeng, ZHANG Yuezhong, CHEN Haoyang. Fuzzing Driver Generation Based on Large Language Models [J]. Computer Science, 2026, 53(6A): 250400113-8.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!