🚀 Awesome Time Series Analysis

📈 A Comprehensive Collection of Papers, Codes & Resources for Time Series Analysis

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🔥 This project collects and organizes high-quality papers and codes for Time Series Analysis (TSA), featuring the latest advances in LLMs, Foundation Models, Graph Neural Networks, and more!

✨ Key Features

• 🎯 Comprehensive Coverage: Forecasting, Classification, Imputation, Anomaly Detection
• 🏢 Multi-domain Applications: Finance, Healthcare, Energy, Transportation
• 📊 Systematic Organization: Well-structured taxonomy and unified workflows
• 🔄 Regular Updates: Keep up with the latest research developments

🔥 Collaboration:
If you notice any missing content or would like to contribute, please feel free to reach out!

✨ Recent Update (June 2026)

Updated the papers and figures.

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✨ Recent Update (April 5, 2026)

We have added ~40 new papers covering the latest advances from ICLR 2026, ICML 2025, NeurIPS 2025, KDD 2025/2026, AAAI 2026, IJCAI 2025, and more. Key additions include:

• New LLM-based methods: SE-LLM (ICLR 2026), FreqLLM (IJCAI 2025)
• Foundation models: Chronos-2, Moirai 2.0, Aurora (ICLR 2026), TimeDiT (KDD 2025), TimeHF, Toto
• New Transformer architectures: DUET (KDD 2025), TFPS (NeurIPS 2025), TimeDistill (KDD 2026)
• Vision-Language models for TS: VLM4TS (AAAI 2026 Oral), OccamVTS (AAAI 2026)
• Multiple new surveys and benchmarks

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📜 Previous Update (October 17, 2025)

💎 1. Our work was accepted by TKDE

We are excited to announce that our paper "Financial Time Series Prediction With Multi-Granularity Graph Augmented Learning" has been accepted for publication in IEEE Transactions on Knowledge and Data Engineering (TKDE) !

💎 2. New Survey Released

We are excited to announce our latest survey: "Large Language Models for Time Series Analysis: Methodologies, Applications, and Emerging Challenges".

📄 The PDF version of this paper can be found in the papers/ directory of this project

This survey highlights these key contributions:

• Roles-Based Taxonomy & Unified Workflows
  We systematically categorize the roles assumed by LLMs in TSA and abstract unified workflows for each role, clarifying their core functionalities and diverse contributions to the field.

• Mechanism-Centric Analysis of Applications
  We comprehensively review representative applications across multiple domains and categorize these applications based on the distinct mechanisms through which LLMs enhance domain-specific tasks, offering new perspectives and insights for the advancement of these downstream tasks.

• Limitations & Future Directions
  We critically examine the key limitations and open challenges in deploying LLMs for TSA and propose prospective research directions to address these challenges and advance the field.

💎 3. Project Structure Update

We have restructured this repository for improved clarity and usability:

• Section A: Large Language Models — Dedicated to resources and research related to LLMs for TSA.
• Section B: Foundation Models — Focused on foundation models for TSA.

💎 4. Literature Update

We have updated the literature collection, adding several outstanding and recent papers to further enrich the repository.

If you find this project helpful, please don't forget to give it a ⭐ Star to show your support. Thank you!

Contents

• A. Large Language Models
  • Taxonomy of Roles and Unified Workflows
    • Role 1️: Direct Inference Engines
    • Role 2️: Static Feature Enhancers
    • Role 3️: Dynamic Task Controllers
  • Application
    • Finance
    • Traffic
    • Energy
    • Others
• B. Foundation Models
• C. Graph Neural Network-based Models
• D. Reinforcement Learning-based Models
• E. Transformer-based Models
• F. Generative Methods based Models
• G. Classical Time Series Models
• H. Quantitative Open Sourced Framework
• I. Alpha Factor Mining
• J. Survey
• 📚 Citation
  • 👥 Contributors
  • 📄 License
  • ⭐ Star History

A. Large Language Models

Figure 1: Frameworks of our new work "Large Language Models for Time Series Analysis: Methodologies, Applications, and Emerging Challenges".

Taxonomy of Roles and Unified Workflows

Figure 2: Architectural diagrams of the three roles — (a) Direct Inference Engine, (b) Static Feature Enhancer, and (c) Dynamic Task Controller — illustrating how deeply the LLM is integrated into the TSA pipeline.

Model	Task	Role	Tokenization	Prompt	Semantic Alignment	Fine-tuning	Code
OFA	General	IE	Patch-level	Vector-based	Emb-Injected	Endogenous(Direct)	✅
aLLM4TS	Forecasting	IE	Patch-level	Vector-based	-	Hybrid(Direct)	✅
PromptCast	Forecasting	IE	Digit-level	Text-based	-	-	✅
TimeLLM	Forecasting	IE	Patch-level	Vector-based	Emb-Injected	Exogenous(Direct)	✅
UniTime	Forecasting	IE	Patch-level	Vector-based	Emb-Injected	Exogenous(Direct)	✅
AutoTimes	Forecasting	IE	Patch-level	Vector-based	-	Hybrid(Direct)	✅
S2IP-LLM	Forecasting	IE	Patch-level	Vector-based	Distributional	Exogenous(Direct)	✅
ETP	Classification	IE	-	Vector-based	Contrastive	Exogenous(Direct)	❌
TENT	Classification	IE	-	Vector-based	Contrastive	Exogenous(Direct)	❌
Qiu et al.	Classification	IE	-	Vector-based	Distributional	-	❌
MTAM	Classification	IE	Patch-level	-	Distributional	-	❌
TimeCMA	Forecasting	IE	Digit-level	Text-based	Distributional	Exogenous(Direct)	✅
TableTime	Classification	IE	Digit-level	Text-based	Distributional	Exogenous(Direct)	✅
MedualTime	Classification	IE	Digit-level	Text-based	Emb-Injected	Exogenous(Direct)	✅
METS	Classification	E	-	-	Contrastive	-	✅
Xie et al.	Forecasting	E	-	Text-based	-	Multi-modal Fusion	❌
TimeReasoner	Forecasting	E	-	Text-based	-	-	❌
Time-R1	Forecasting	E	-	Text-based	-	-	❌
Time-RA	Anomaly Detection	E	-	-	-	-	✅
TEMPO	Forecasting	IE+E	Patch-level	Vector-based	Emb-Injected	Hybrid(LoRA)	✅
LLM4TS	Forecasting	IE+E	Patch-level	Vector-based	-	Endogenous(LoRA)	✅
TEST	General	IE+E	Patch-level	Vector-based	Contrastive	Exogenous(Direct)	✅
Chronos	General	IE+E	Bin-level	Vector-based	-	Exogenous(Direct)	✅
LLM-Mob	Forecasting	IE+E	Digit-level	Text-based	-	-	✅
TimeCAP	Forecasting	IE+E	-	Text-based	-	Exogenous(Direct)	✅
TS-Reasoner	Forecasting	HC	-	Text-based	-	-	❌
AuxMobLCast	Forecasting	HC	Digit-level	Vector-based	-	-	❌
LAMP	Forecasting	HC	-	Text-based	-	-	❌
LA-GCN	Forecasting	HC	Digit-level	Text-based	-	-	❌
Chen et al.	Forecasting	HC	-	Text-based	-	-	❌
Park et al.	Anomaly Detection	HC	-	Text-based	-	-	❌
Zuo et al.	Forecasting	HC	-	Text-based	-	-	❌
DualSG	Forecasting	HC	-	Text-based	-	Exogenous(Direct)	✅
SE-LLM	Forecasting	IE	Patch-level	Vector-based	Emb-Injected	Exogenous(Direct)	❌
FreqLLM	Forecasting	IE	Patch-level	Vector-based	Distributional	Exogenous(Direct)	✅
LLM-TPF	Forecasting	IE	Patch-level	Vector-based	Distributional	Exogenous(Direct)	❌
LLM4FTS	Forecasting	IE+E	Patch-level	Text-based	-	Exogenous(Direct)	❌
VLM4TS	Anomaly Detection	E	-	Text-based	-	-	✅
OccamVTS	Forecasting	E	-	-	-	Exogenous(Direct)	❌

_{Note: Roles follow the survey taxonomy — IE = Inference Engine (Direct Inference Engine), E = Enhancer (Static Feature Enhancer), IE+E = the combination of the two, and HC = Hybrid Collaborator (Dynamic Task Controller).}

Role 1️: Direct Inference Engines

(a.k.a. Fine-tune-based Inference Engines)

Tokenization Approaches

Digit-level Tokenization

• Llama 2: Open Foundation and Fine-Tuned Chat Models
  Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample
  arXiv, 2023.
  Paper

• MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learning
  Ye, Jiexia and Zhang, Weiqi and Li, Ziyue and Li, Jia and Zhao, Meng and Tsung, Fugee
  IJCAI 2025.
  Paper | Code

• PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting
  Hao Xue, Flora D Salim
  IEEE TKDE, 2023.
  Paper | Code

• LLM-ABBA: Understanding Time Series via Symbolic Approximation
  Xinye Chen, Erin Carson, Cheng Kang
  IEEE Transactions on Signal Processing, 2026.
  Paper

Patch-level Tokenization

• One fits all: Power general time series analysis by pretrained LM
  Tian Zhou, Peisong Niu, Liang Sun, Rong Jin, et al.
  NeurIPS 2023.
  Paper | Code

• Multi-patch prediction: Adapting language models for time series representation learning
  Yuxuan Bian, Xuan Ju, Jiangtong Li, Zhijian Xu, Dawei Cheng, Qiang Xu
  ICML 2024.
  Paper | Code

• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
  Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, Qingsong Wen
  ICLR 2024.
  Paper | Code

• UniTime: A language-empowered unified model for cross-domain time series forecasting
  Xu Liu, Junfeng Hu, Yuan Li, Shizhe Diao, Yuxuan Liang, Bryan Hooi, Roger Zimmermann
  WWW 2024.
  Paper | Code

• SE-LLM: Semantic-Enhanced Time-Series Forecasting via Large Language Models
  ICLR 2026.
  Paper

• FreqLLM: Frequency-Aware Large Language Models for Time Series Forecasting
  Shunnan Wang et al.
  IJCAI 2025.
  Paper | Code

• LLM-TPF: Multiscale Temporal Periodicity-Semantic Fusion LLMs for Time Series Forecasting
  Qihong Pan et al.
  IJCAI 2025.
  Paper

Bin-level Tokenization

• Chronos: Learning the Language of Time Series
  Abdul Fatir Ansari, Lorenzo Stella, Ali Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Hao Wang, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Bernie Wang
  TMLR 2024.
  Paper | Code

Prompt Engineering

Text-based Prompt

• PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting
  Hao Xue, Flora D Salim
  IEEE TKDE, 2023.
  Paper | Code

• Leveraging language foundation models for human mobility forecasting
  Hao Xue, Bhanu Prakash Voutharoja, Flora D Salim
  SIGSPATIAL 2022.
  Paper

• Where would I go next? Large language models as human mobility predictors
  Xinglei Wang, Meng Fang, Zichao Zeng, Tao Cheng
  arXiv 2023.
  Paper | Code

• LST-Prompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting
  Haoxin Liu, Zhiyuan Zhao, Jindong Wang, Harshavardhan Kamarthi, B Aditya Prakash
  ACL 2024.
  Paper | Code

• TableTime: Reformulating Time Series Classification as Training-Free Table Understanding with Large Language Models
  Jiahao Wang, Mingyue Cheng, Qingyang Mao, Yitong Zhou, Feiyang Xu, Xin Li
  CIKM 2025.
  Paper

• The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges
  Qianqian Xie, Weiguang Han, Yanzhao Lai, Min Peng, Jimin Huang
  arXiv 2023.
  Paper

• Rethinking the Role of LLMs in Time Series Forecasting
  Xin Qiu et al.
  arXiv 2026.
  Paper

Vector-based (Soft) Prompt

• One fits all: Power general time series analysis by pretrained LM
  Tian Zhou, Peisong Niu, Liang Sun, Rong Jin, et al.
  NeurIPS 2023.
  Paper | Code

• TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series
  Chenxi Sun, Hongyan Li, Yaliang Li, Shenda Hong
  ICLR 2024.
  Paper

• TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting
  Defu Cao, Furong Jia, Sercan Ö. Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, Yan Liu
  ICLR 2024.
  Paper | Code

• S²IP-LLM: Semantic space informed prompt learning with LLM for time series forecasting
  Zijie Pan, Yushan Jiang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, Dongjin Song
  ICML 2024.
  Paper | Code

• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
  Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, Qingsong Wen
  ICLR 2024.
  Paper | Code

• AutoTimes: Autoregressive time series forecasters via large language models
  Yong Liu, Guo Qin, Xiangdong Huang, Jianmin Wang, Mingsheng Long
  NeurIPS 2024.
  Paper | Code

• Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis
  Wen Ye, Wei Yang, Defu Cao, Yizhou Zhang, Lumingyuan Tang, Jie Cai, Yan Liu
  arXiv 2024.
  Paper

Semantic Alignment

Contrastive Alignment

• ETP: Learning transferable ECG representations via ECG-text pre-training
  Che Liu, Zhongwei Wan, Sibo Cheng, Mi Zhang, Rossella Arcucci
  ICASSP 2024.
  Paper

• TimeCMA: Towards LLM-empowered multivariate time series forecasting via cross-modality alignment
  Chenxi Liu, Qianxiong Xu, Hao Miao, Sun Yang, Lingzheng Zhang, Cheng Long, Ziyue Li, Rui Zhao
  AAAI 2025.
  Paper | Code

• Can brain signals reveal inner alignment with human languages?
  Jielin Qiu, William Han, Jiacheng Zhu, Mengdi Xu, Douglas Weber, Bo Li, Ding Zhao
  EMNLP 2023.
  Paper | Code

Distributional Alignment

• Transfer knowledge from natural language to electrocardiography: Can we detect cardiovascular disease through language models?
  Jielin Qiu, William Han, Jiacheng Zhu, Mengdi Xu, Michael Rosenberg, Emerson Liu, Douglas Weber, Ding Zhao
  Findings of EACL, 2023.
  Paper | Code

• S²IP-LLM: Semantic space informed prompt learning with LLM for time series forecasting
  Zijie Pan, Yushan Jiang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, Dongjin Song
  ICML 2024.
  Paper | Code

Embedding-Injected Alignment

• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
  Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, Qingsong Wen
  ICLR 2024.
  Paper | Code

• TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series
  Chenxi Sun, Hongyan Li, Yaliang Li, Shenda Hong
  ICLR 2024.
  Paper

• Tent: Connect language models with IoT sensors for zero-shot activity recognition
  Yunjiao Zhou, Jianfei Yang, Han Zou, Lihua Xie
  IEEE Transactions on Mobile Computing, 2026.
  Paper

Fine-tuning

Parameters Selection

Endogenous Parameters

• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
  Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, Qingsong Wen
  ICLR 2024.
  Paper | Code

• Multi-patch prediction: Adapting language models for time series representation learning
  Yuxuan Bian, Xuan Ju, Jiangtong Li, Zhijian Xu, Dawei Cheng, Qiang Xu
  ICML 2024.
  Paper | Code

Exogenous Parameters

• S²IP-LLM: Semantic space informed prompt learning with LLM for time series forecasting
  Zijie Pan, Yushan Jiang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, Dongjin Song
  ICML 2024.
  Paper | Code

• TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series
  Chenxi Sun, Hongyan Li, Yaliang Li, Shenda Hong
  ICLR 2024.
  Paper

Hybrid Parameters

• TimeCMA: Towards LLM-empowered multivariate time series forecasting via cross-modality alignment
  Chenxi Liu, Qianxiong Xu, Hao Miao, Sun Yang, Lingzheng Zhang, Cheng Long, Ziyue Li, Rui Zhao
  AAAI 2025.
  Paper | Code

• UniTime: A language-empowered unified model for cross-domain time series forecasting
  Xu Liu, Junfeng Hu, Yuan Li, Shizhe Diao, Yuxuan Liang, Bryan Hooi, Roger Zimmermann
  WWW 2024.
  Paper | Code

Fine-tuning Strategies Selection

Direct Fine-tuning

• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
  Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, Qingsong Wen
  ICLR 2024.
  Paper | Code

• A Decoder-Only Foundation Model for Time-Series Forecasting
  Das, Abhimanyu and Kong, Weihao and Sen, Rajat and Zhou, Yichen
  ICML, 2024.
  Paper | Code

• Multi-patch prediction: Adapting language models for time series representation learning
  Yuxuan Bian, Xuan Ju, Jiangtong Li, Zhijian Xu, Dawei Cheng, Qiang Xu
  ICML 2024.
  Paper | Code

LoRA-based Fine-tuning

• UniTime: A language-empowered unified model for cross-domain time series forecasting
  Xu Liu, Junfeng Hu, Yuan Li, Shizhe Diao, Yuxuan Liang, Bryan Hooi, Roger Zimmermann
  WWW 2024.
  Paper | Code

• TimeCMA: Towards LLM-empowered multivariate time series forecasting via cross-modality alignment
  Chenxi Liu, Qianxiong Xu, Hao Miao, Sun Yang, Lingzheng Zhang, Cheng Long, Ziyue Li, Rui Zhao
  AAAI 2025.
  Paper | Code

• S²IP-LLM: Semantic space informed prompt learning with LLM for time series forecasting
  Zijie Pan, Yushan Jiang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, Dongjin Song
  ICML 2024.
  Paper | Code

Role 2️: Static Feature Enhancers

(a.k.a. Enhancer based on TSA Methods)

Enhancement of Time Series Data

Self-Supervised Learning

• LLM4TS: Aligning pre-trained LLMs as data-efficient time-series forecasters
  Ching Chang, Wei-Yao Wang, Wen-Chih Peng, Tien-Fu Chen
  ACM Transactions on Intelligent Systems and Technology 2025.
  Paper | Code

• Multi-patch prediction: Adapting language models for time series representation learning
  Yuxuan Bian, Xuan Ju, Jiangtong Li, Zhijian Xu, Dawei Cheng, Qiang Xu
  ICML 2024.
  Paper | Code

Synthetic Data Generation

• Chronos: Learning the Language of Time Series
  Abdul Fatir Ansari, Lorenzo Stella, Ali Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Hao Wang, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Bernie Wang
  TMLR 2024.
  Paper | Code

• TimeCAP: Learning to contextualize, augment, and predict time series events with large language model agents
  Geon Lee, Wenchao Yu, Kijung Shin, Wei Cheng, Haifeng Chen
  AAAI 2025.
  Paper | Code

Multi-Modal Data Fusion

• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models
  Alejandro Lopez-Lira, Yuehua Tang
  arXiv 2023. [Paper]

• Frozen language model helps ECG zero-shot learning
  Jun Li, Che Liu, Sibo Cheng, Rossella Arcucci, Shenda Hong
  MIDL, 2023.
  Paper

• TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting
  Defu Cao, Furong Jia, Sercan Ö. Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, Yan Liu
  ICLR 2024.
  Paper | Code

• GPT4MTS: Prompt-based Large Language Model for Multimodal Time-series Forecasting
  Furong Jia, Kevin Wang, Yixiang Zheng, Defu Cao, Yan Liu
  AAAI, 2024.
  Paper

• VLM4TS: Harnessing Vision-Language Models for Time Series Anomaly Detection
  Zelin He et al.
  AAAI 2026 (Oral).
  Paper | Code

• OccamVTS: Distilling Vision Models to 1% Parameters for Time Series Forecasting
  AAAI 2026.
  Paper

• UniCast: A Unified Framework for Instance-Conditioned Multimodal Time-Series Forecasting
  arXiv 2025.
  Paper

• AV2TS: A Multivariate Time Series Modeling Framework for Audio-Visual Segmentation
  Li Shen, Yangzhu Wang, Xuyi Fan, Yuning Wei, Huaxin Qiu
  IEEE Transactions on Multimedia, 2026.
  Paper

Enhancement of Interpretability

• Can "Slow-thinking" LLMs Make Time Series Predictions More Reliable? Enhancing LLM-based Time Series Forecasting via Chain-of-Thought Prompting
  Shuai Wang, Qing Li, Chenyang Shang, Yushu Chen, Zhenyu Liu, Xiang Li, Shenda Hong
  arXiv 2025.
  Paper | Code

• Time-R1: Towards Comprehensive Temporal Reasoning in LLMs
  Zijia Liu, Peixuan Han, Haofei Yu, Haoru Li, Jiaxuan You
  arXiv 2025.
  Paper

• Where would I go next? Large language models as human mobility predictors
  Xinglei Wang, Meng Fang, Zichao Zeng, Tao Cheng
  arXiv 2023.
  Paper | Code

• Time-RA: Towards Time Series Reasoning for Anomaly with LLM Feedback
  Yiyuan Yang, Zichuan Liu, Lei Song, Kai Ying, Zhiguang Wang, Tom Bamford, Svitlana Vyetrenko, Jiang Bian, Qingsong Wen
  arXiv 2025.
  Paper

Role 3️: Dynamic Task Controllers

(a.k.a. Hybrid Collaborators)

• Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis
  Wen Ye, Wei Yang, Defu Cao, Yizhou Zhang, Lumingyuan Tang, Jie Cai, Yan Liu
  arXiv 2024.
  Paper

• Language models can improve event prediction by few-shot abductive reasoning
  Xiaoming Shi, Siqiao Xue, Kangrui Wang, Fan Zhou, James Zhang, Jun Zhou, Chenhao Tan, Hongyuan Mei
  NeurIPS 2023.
  Paper

• Leveraging language foundation models for human mobility forecasting
  Hao Xue, Bhanu Prakash Voutharoja, Flora D Salim
  SIGSPATIAL 2022.
  Paper

• ChatGPT Informed Graph Neural Network for Stock Movement Prediction
  Zihan Chen, Lei Nico Zheng, Cheng Lu, Jialu Yuan, Di Zhu
  arXiv 2023.
  Paper | Code

• Language knowledge-assisted representation learning for skeleton-based action recognition
  Haojun Xu, Yan Gao, Zheng Hui, Jie Li, Xinbo Gao
  IEEE Transactions on Multimedia 2025.
  Paper

• DualSG: A Dual-Stream Explicit Semantic-Guided Multivariate Time Series Forecasting Framework
  Kuiye Ding, Fanda Fan, Yao Wang, Xiaorui Wang, Luqi Gong, Yishan Jiang, others
  arXiv 2025.
  Paper | Code

• Enhancing Anomaly Detection in Financial Markets with an LLM-based Multi-Agent Framework
  Taejin Park
  arXiv 2024.
  Paper

• Large Language Model-Empowered Interactive Load Forecasting
  Yu Zuo, Dalin Qin, Yi Wang
  arXiv 2025.
  Paper

Application

Figure 3: An overview of the application of LLMs for TSA.

Finance

Stock Movement (Trend) Forecasting

• ChatGPT Informed Graph Neural Network for Stock Movement Prediction
  Zihan Chen, Lei Nico Zheng, Cheng Lu, Jialu Yuan, Di Zhu
  arXiv 2023.
  Paper | Code

• Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction
  Yujie Ding, Shuai Jia, Tianyi Ma, Bingcheng Mao, Xiuze Zhou, Liuliu Li, Dongming Han
  arXiv 2023.
  Paper

• Ploutos: Towards Interpretable Stock Movement Prediction with Financial Large Language Model
  Hanshuang Tong, Jun Li, Ning Wu, Ming Gong, Dongmei Zhang, Qi Zhang arXiv 2024.
  Paper

• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models
  Alejandro Lopez-Lira, Yuehua Tang
  arXiv 2023.
  Paper

• The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges
  Qianqian Xie, Weiguang Han, Yanzhao Lai, Min Peng, Jimin Huang
  arXiv 2023.
  Paper

• LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction
  Meiyun Wang, Kiyoshi Izumi, Hiroki Sakaji
  ACL 2024.
  Paper

• Learning to generate explainable stock predictions using self-reflective large language models
  Kelvin JL Koa, Yunshan Ma, Ritchie Ng, Tat-Seng Chua ACM Web Conference 2024.
  Paper | Code

• Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow
  Tian Guo, Emmanuel Hauptmann arXiv 2024.
  Paper

Stock Price Forecasting

• Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting
  Xinli Yu, Zheng Chen, Yuan Ling, Shujing Dong, Zongyi Liu, Yanbin Lu
  EMNLP 2023.
  Paper

• Leveraging Vision-Language Models for Granular Market Change Prediction
  Christopher Wimmer, Navid Rekabsaz
  arXiv 2023.
  Paper

• StockTime: A Time Series Specialized Large Language Model Architecture for Stock Price Prediction
  Shengkun Wang, Taoran Ji, Linhan Wang, Yanshen Sun, Shang-Ching Liu, Amit Kumar, Chang-Tien Lu
  arXiv 2024.
  Paper

• LLM4FTS: Enhancing Large Language Models for Financial Time Series Prediction
  Zian Liu, Renjun Jia
  arXiv 2025.
  Paper

• Retrieval-augmented Large Language Models for Financial Time Series Forecasting
  arXiv 2025.
  Paper

• Dual Adaptation of Time-Series Foundation Models for Financial Forecasting
  ICML 2025.
  Paper

Traffic

Traffic Flow Forecasting

• How can large language models understand spatial-temporal data?
  Lei Liu, Shuo Yu, Runze Wang, Zhenxun Ma, Yanming Shen
  arXiv 2024.
  Paper

• LLM-TFP: Integrating large language models with spatio-temporal features for urban traffic flow prediction
  Haitao Cheng, Zibin Gong, Chang Wang
  Applied Soft Computing, 2025.
  Paper

• Edge computing enabled large-scale traffic flow prediction with GPT in intelligent autonomous transport system for 6G network
  Yi Rong, Yingchi Mao, Huajun Cui, Xiaoming He, Mingkai Chen
  IEEE Transactions on Intelligent Transportation Systems (TITS), 2024.
  Paper

• Spatial-Temporal Large Language Model for Traffic Prediction
  Chenxi Liu, Sun Yang, Qianxiong Xu, Zhishuai Li, Cheng Long, Ziyue Li, Rui Zhao
  arXiv 2024.
  Paper | Code

• ST-LLM+: Graph Enhanced Spatio-Temporal Large Language Models for Traffic Prediction
  Chenxi Liu, Kethmi Hirushini Hettige, Qianxiong Xu, Cheng Long, Shili Xiang, Gao Cong, Ziyue Li, Rui Zhao
  IEEE Transactions on Knowledge and Data Engineering, 2025.
  Paper

• GPT4TFP: Spatio-temporal fusion large language model for traffic flow prediction
  Yiwu Xu, Mengchi Liu
  Neurocomputing, 2025.
  Paper

• TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models
  Yilong Ren, Yue Chen, Shuai Liu, Boyue Wang, Haiyang Yu, Zhiyuan Liu
  arXiv 2024.
  Paper

• TrafficBERT: Pre-trained model with large-scale permuted traffic data for long-term traffic forecasting
  Daejin Kim, Youngin Cho, Dongmin Kim, Cheonbok Park, Jaegul Choo
  Expert Systems with Applications, 2021.
  Paper

• Embracing large language models in traffic flow forecasting
  Yusheng Zhao, Xiao Luo, Haomin Wen, Zhiping Xiao, Wei Ju, Ming Zhang
  Findings of ACL, 2025.
  Paper

• TrafficGPT: Viewing, processing and interacting with traffic foundation models
  Siyao Zhang, Daocheng Fu, Wenzhe Liang, Zhao Zhang, Bin Yu, Pinlong Cai, Baozhen Yao
  Transport Policy, 2024.
  Paper | Code

• Emergency Events Traffic Flow Forecasting Using Text-Prompt-Guided Multimodal Large Language Models
  Yaxuan Lu, Guangyu Huo, Xiaohui Cui, Boyue Wang, Yong Zhang, Zhiyong Cui
  IEEE Transactions on Intelligent Transportation Systems, 2026.
  Paper

Human Mobility Forecasting

• Exploring large language models for human mobility prediction under public events
  Yuebing Liang, Yichao Liu, Xiaohan Wang, Zhan Zhao
  Computers, Environment and Urban Systems, 2024.
  Paper

• Mobility-llm: Learning visiting intentions and travel preference from human mobility data with large language models
  Letian Gong, Yan Lin, Yiwen Lu, Xuedi Han, Yichen Liu, Shengnan Guo, Youfang Lin, Huaiyu Wan, et al.
  NeurIPS, 2024.
  Paper

• Where would I go next? Large language models as human mobility predictors
  Xinglei Wang, Meng Fang, Zichao Zeng, Tao Cheng
  arXiv 2023.
  Paper | Code

• Toward interactive next location prediction driven by large language models
  Yong Chen, Ben Chi, Chuanjia Li, Yuliang Zhang, Chenlei Liao, Xiqun Chen, Na Xie
  IEEE Transactions on Computational Social Systems, 2025.
  Paper

• Large Language Models for Spatial Trajectory Patterns Mining
  Zheng Zhang, Hossein Amiri, Zhenke Liu, Liang Zhao, Andreas Zuefle
  SIGSPATIAL, 2024.
  Paper | Code

• Leveraging language foundation models for human mobility forecasting
  Hao Xue, Bhanu Prakash Voutharoja, Flora D Salim
  SIGSPATIAL, 2022.
  Paper

• UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models
  Yifei Jiang, Xinyan Zhu, Jiayu Fan, Hua Wei
  EMNLP 2024.
  Paper | Code

Energy

Power Load Forecasting

• MMGPT4LF: Leveraging an optimized pre-trained GPT-2 model with multi-modal cross-attention for load forecasting
  Mingyang Gao, Suyang Zhou, Wei Gu, Zhi Wu, Haiquan Liu, Aihua Zhou, Xinliang Wang
  Applied Energy, 2025.
  Paper

• A general framework for load forecasting based on pre-trained large language model
  Mingyang Gao, Suyang Zhou, Wei Gu, Zhi Wu, Haiquan Liu, Aihua Zhou
  arXiv 2024.
  Paper

• Utilizing language models for energy load forecasting
  Hao Xue, Flora D. Salim
  In Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, 2023.
  Paper

• Empower pre-trained large language models for building-level load forecasting
  Yating Zhou, Meng Wang
  IEEE Transactions on Power Systems, 2025.
  Paper

• TimeGPT in load forecasting: A large time series model perspective
  Wenlong Liao, Shouxiang Wang, Dechang Yang, Zhe Yang, Jiannong Fang, Christian Rehtanz, Fernando Porté-Agel
  Applied Energy, 2025.
  Paper

• Large Language Model-Empowered Interactive Load Forecasting
  Yu Zuo, Dalin Qin, Yi Wang
  arXiv, 2025.
  Paper

Climate (Weather) Forecasting

• WeatherQA: Can multimodal language models reason about severe weather?
  Chengqian Ma, Zhanxiang Hua, Alexandra Anderson-Frey, Vikram Iyer, Xin Liu, Lianhui Qin
  arXiv, 2024.
  Paper | Code

• ClimaX: A foundation model for weather and climate
  Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K Gupta, Aditya Grover
  ICML, 2023.
  Paper | Code

• Climatellm: Efficient weather forecasting via frequency-aware large language models
  Shixuan Li, Wei Yang, Peiyu Zhang, Xiongye Xiao, Defu Cao, Yuehan Qin, Xiaole Zhang, Yue Zhao, Paul Bogdan
  arXiv, 2025.
  Paper

• STELLM: Spatio-temporal enhanced pre-trained large language model for wind speed forecasting
  Tangjie Wu, Qiang Ling
  Applied Energy, 2024.
  Paper

• GLALLM: Adapting LLMs for spatio-temporal wind speed forecasting via global-local aware modeling
  Tangjie Wu, Qiang Ling
  Knowledge-Based Systems, 2025.
  Paper

• EF-LLM: Energy forecasting LLM with AI-assisted automation, enhanced sparse prediction, hallucination detection
  Zihang Qiu, Chaojie Li, Zhongyang Wang, Renyou Xie, Borui Zhang, Huadong Mo, Guo Chen, Zhaoyang Dong
  arXiv, 2024.
  Paper

Others

• Frozen language model helps ECG zero-shot learning
  Jun Li, Che Liu, Sibo Cheng, Rossella Arcucci, Shenda Hong
  MIDL, 2023.
  Paper

• Health system-scale language models are all-purpose prediction engines
  Jiang, Lavender Yao; Liu, Xujin Chris; Nejatian, Nima Pour; Nasir-Moin, Mustafa; Wang, Duo; Abidin, Anas; Eaton, Kevin; Riina, Howard Antony; Laufer, Ilya; Punjabi, Paawan Nature, 2023.
  Paper

• MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learning
  Ye, Jiexia; Zhang, Weiqi; Li, Ziyue; Li, Jia; Zhao, Meng; Tsung, Fugee
  IJCAI 2025.
  Paper | Code

• Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction
  Chen Chen, Lei Li, Marcel Beetz, Abhirup Banerjee, Ramneek Gupta, Vicente Grau
  IEEE Transactions on Big Data, 2025.
  Paper

B. Foundation Models

• TimeGPT-1
  Garza, Azul and Mergenthaler-Canseco, Max
  arXiv, 2023.
  Paper | Code

• Timer: Generative Pre-trained Transformers are Large Time Series Models
  Liu, Yong and Zhang, Haoran and Li, Chenyu and Huang, Xiangdong and Wang, Jianmin and Long, Mingsheng
  PMLR, 2024.
  Paper | Code

• A Decoder-Only Foundation Model for Time-Series Forecasting
  Das, Abhimanyu and Kong, Weihao and Sen, Rajat and Zhou, Yichen
  ICML, 2024.
  Paper | Code

• Lag-Llama: Towards Foundation Models for Time Series Forecasting
  Rasul, Kashif and Ashok, Arjun and Williams, Andrew Robert and Khorasani, Arian and Adamopoulos, George and Bhagwatkar, Rishika and Biloš, Marin and Ghonia, Hena and Hassen, Nadhir and Schneider, Anderson, et al.
  arXiv, 2023.
  Paper | Code

• MOMENT: A Family of Open Time-series Foundation Models
  Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, Artur Dubrawski
  ICML, 2024.
  Paper | Codes

• Unified Training of Universal Time Series Forecasting Transformers
  Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo
  ICML, 2024.
  Paper | Code

• Chronos-2: From Univariate to Universal Forecasting
  Abdul Fatir Ansari et al.
  arXiv 2025.
  Paper | Code

• ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables
  Sebastian Pineda Arango et al.
  arXiv 2025.
  Paper

• Moirai 2.0: When Less Is More for Time Series Forecasting
  Salesforce AI Research
  arXiv 2025.
  Paper

• Aurora: Towards Universal Generative Multimodal Time Series Forecasting
  ICLR 2026.
  Paper | Code

• TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model
  KDD 2025.
  Paper

• Toto: Time Series Optimized Transformer for Observability
  Datadog
  arXiv 2025.
  Paper | Code

• TimeHF: Billion-Scale Time Series Models Guided by Human Feedback
  arXiv 2025.
  Paper

• Xihe: Scalable Zero-Shot Time Series Learner via Hierarchical Interleaved Block Attention
  Yinbo Sun et al.
  arXiv 2025.
  Paper

• Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting
  Xinghong Fu et al.
  arXiv 2026.
  Paper | Code

• WaveToken: Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization
  Amazon Science
  ICML 2025.
  Paper

• In-Context Fine-Tuning for Time-Series Foundation Models
  ICML 2025.
  Paper

• AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting
  ICML 2025.
  Paper

• ELF: Lightweight Online Adaptation for Time Series Foundation Model Forecasts
  ICML 2025.
  Paper

• Are Time Series Foundation Models Ready for Zero-Shot Forecasting?
  ICML 2025.
  Paper

• TimeRAF: Retrieval-Augmented Foundation Model for Zero-Shot Time Series Forecasting
  Huanyu Zhang, Chang Xu, Yi-Fan Zhang, Zhang Zhang, Liang Wang, Jiang Bian
  IEEE Transactions on Knowledge and Data Engineering, 2025.
  Paper

• MetaIndux-TS: Frequency-Aware AIGC Foundation Model for Industrial Time Series
  Haiteng Wang, Lei Ren, Yikang Li, Yuqing Wang
  IEEE Transactions on Neural Networks and Learning Systems, 2025.
  Paper

• Bridging Distribution Gaps in Time Series Foundation Model Pretraining With Prototype-Guided Normalization
  Peiliang Gong, Emadeldeen Eldele, Min Wu, Zhenghua Chen, Xiaoli Li, Daoqiang Zhang
  IEEE Transactions on Neural Networks and Learning Systems, 2026.
  Paper

C. Graph Neural Network-based Models

• MCI-GRU: Stock Prediction Model Based on Multi-head Cross-attention and Improved GRU
  Neurocomputing, 2025.
  Paper

• FinMamba: Market-Aware Graph Enhanced Multi-Level Mamba for Stock Movement Prediction
  Yifan Hu, Peiyuan Liu, Yuante Li, Dawei Cheng, Naiqi Li, Tao Dai, Jigang Bao, Shu-Tao Xia
  arXiv 2025.
  Paper

• LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU
  Peng Zhu, Yuante Li, Yifan Hu, Qinyuan Liu, Dawei Cheng, Yuqi Liang
  CIKM, 2024.
  Paper | Codes

• Automatic De-Biased Temporal-Relational Modeling for Stock Investment Recommendation
  Weijun Chen, Shun Li, Xipu Yu, Heyuan Wang, Wei Chen, Tengjiao Wang
  IJCAI, 2024.
  Paper

• MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction
  Hao Qian, Hongting Zhou, Qian Zhao, Hao Chen, Hongxiang Yao, Jingwei Wang, Ziqi Liu, Fei Yu, Zhiqiang Zhang, Jun Zhou
  AAAI, 2024.
  Paper

• ECHO-GL: Earnings Calls-Driven Heterogeneous Graph Learning for Stock Movement Prediction
  Mengpu Liu, Mengying Zhu, Xiuyuan Wang, Guofang Ma, Jianwei Yin, Xiaolin Zheng
  AAAI, 2024.
  Paper | Codes

• TCGPN: Temporal-Correlation Graph Pre-trained Network for Stock Forecasting
  Wenbo Yan, Ying Tan
  arXiv, 2024.
  Paper

• Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction
  Sheng Xiang, Dawei Cheng, Chencheng Shang, Ying Zhang, Yuqi Liang
  CIKM, 2022.
  Paper | Codes

• Relational Temporal Graph Convolutional Networks for Ranking-Based Stock Prediction
  Zetao Zheng, Jie Shao, Jia Zhu, Heng Tao Shen
  ICDE, 2023.
  Paper | Codes

• Temporal-Relational hypergraph tri-Attention networks for stock trend prediction
  Chaoran Cui, Xiaojie Li, Chunyun Zhang, Weili Guan, Meng Wang
  Pattern Recognition, 2023.
  Paper | Codes

• Financial time series forecasting with multi-modality graph neural network
  Dawei Cheng, Fangzhou Yang, Sheng Xiang, Jin Liu
  Pattern Recognition, 2022.
  Paper | Codes

• Hierarchical Adaptive Temporal-Relational Modeling for Stock Trend Prediction
  Heyuan Wang, Shun Li, Tengjiao Wang, Jiayi Zheng
  IJCAI, 2021.
  Paper | Codes

• REST: Relational Event-driven Stock Trend Forecasting
  Wentao Xu, Weiqing Liu, Chang Xu, Jiang Bian, Jian Yin, Tie-Yan Liu
  WWW, 2021.
  Paper

• Knowledge Graph-based Event Embedding Framework for Financial Quantitative Investments
  Dawei Cheng, Fangzhou Yang, Xiaoyang Wang, Ying Zhang, Liqing Zhang
  SIGIR, 2020.
  Paper

D. Reinforcement Learning-based Models

• MacMic: Executing Iceberg Orders via Hierarchical Reinforcement Learning
  Hui Niu, Siyuan Li, Jian Li
  IJCAI, 2024.
  Paper

• Cross-contextual Sequential Optimization via Deep Reinforcement Learning for Algorithmic Trading
  Kaiming Pan, Yifan Hu, Li Han, Haoyu Sun, Dawei Cheng, Yuqi Liang
  CIKM, 2024.
  Paper

• Reinforcement Learning with Maskable Stock Representation for Portfolio Management in Customizable Stock Pools
  Wentao Zhang, Yilei Zhao, Shuo Sun, Jie Ying, Yonggang Xie, Zitao Song, Xinrun Wang, Bo An
  WWW, 2024.
  Paper | Codes

• FreQuant: A Reinforcement-Learning based Adaptive Portfolio Optimization with Multi-frequency Decomposition
  Jeon, Jihyeong; Park, Jiwon; Park, Chanhee; Kang, U
  KDD, 2024.
  Paper

• MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading
  Chuqiao Zong, Chaojie Wang, Molei Qin, Lei Feng, Xinrun Wang, Bo An
  KDD, 2024.
  Paper | Codes

• Asymmetric Graph-Based Deep Reinforcement Learning for Portfolio Optimization
  Haoyu Sun, Xin Liu, Yuxuan Bian, Peng Zhu, Dawei Cheng, Yuqi Liang
  ECML PKDD, 2024.
  Paper

• NGDRL: A Dynamic News Graph-Based Deep Reinforcement Learning Framework for Portfolio Optimization
  Yuxuan Bian, Haoyu Sun, Yang Lei, Peng Zhu, Dawei Cheng
  DASFAA, 2024.
  Paper

• Efficient Continuous Space Policy Optimization for High-frequency Trading
  Li Han, Nan Ding, Guoxuan Wang, Dawei Cheng, Yuqi Liang
  KDD, 2023.
  Paper

• Optimal Action Space Search: An Effective Deep Reinforcement Learning Method for Algorithmic Trading
  Zhongjie Duan, Cen Chen, Dawei Cheng, Yuqi Liang, Weining Qian
  CIKM, 2022.
  Paper | Codes

E. Transformer-based Models

• Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
  ICLR 2026.
  Paper

• TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting
  Yifan Hu, Guibin Zhang, Peiyuan Liu, Disen Lan, Naiqi Li, Dawei Cheng, Tao Dai, Shu-Tao Xia, Shirui Pan
  ICML 2025.
  Paper

• TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting
  Peiyuan Liu, Beiliang Wu, Yifan Hu, Naiqi Li, Tao Dai, Jigang Bao, Shu-Tao Xia
  ICML 2025.
  Paper

• Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting
  Yifan Hu, Peiyuan Liu, Peng Zhu, Dawei Cheng, Tao Dai
  AAAI 2025.
  Paper

• AdaWaveNet: Adaptive Wavelet Network for Non-stationary Time Series Forecasting via End-to-End Learning
  Journal of King Saud University - Computer and Information Sciences, 2026.
  Paper

• DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting
  Xiangfei Qiu et al.
  KDD 2025.
  Paper | Code

• TFPS: Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift
  NeurIPS 2025.
  Paper

• TimeDistill: Efficient Long-Term Time Series Forecasting with MLP via Cross-Architecture Distillation
  KDD 2026.
  Paper | Code

• MASTER: Market-Guided Stock Transformer for Stock Price Forecasting
  Tong Li, Zhaoyang Liu, Yanyan Shen, Xue Wang, Haokun Chen, Sen Huang
  AAAI, 2024.
  Paper | Codes

• CI-STHPAN: Pre-trained Attention Network for Stock Selection with Channel-Independent Spatio-Temporal Hypergraph
  Hongjie Xia, Huijie Ao, Long Li, Yu Liu, Sen Liu, Guangnan Ye, Hongfeng Chai
  AAAI, 2024.
  Paper | Codes

• Predicting stock market trends with self-supervised learning
  Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
  Neurocomputing, 2024.
  Paper

• Multi-scale Time Based Stock Appreciation Ranking Prediction via Price Co-movement Discrimination
  Ruyao Xu, Dawei Cheng, Cen Chen, Siqiang Luo, Yifeng Luo, Weining Qian
  DASFAA, 2022.
  Paper | Codes

• Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport
  Hengxu Lin, Dong Zhou, Weiqing Liu, Jiang Bian
  KDD, 2021.
  Paper | Codes

• Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts
  Jaemin Yoo, Yejun Soun, Yong-chan Park, U Kang
  KDD, 2021.
  Paper | Codes

• Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
  Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, Wancai Zhang
  AAAI, 2021.
  Paper | Code

• iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
  Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, Mingsheng Long
  ICLR, 2024.
  Paper | Code

• A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
  Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam
  ICLR, 2023.
  Paper | Code

F. Generative Methods based Models

• DHMoE: Diffusion Generated Hierarchical Multi-Granular Expertise for Stock Prediction
  Weijun Chen, Yanze Wang
  AAAI, 2025.
  Paper

• Market-GAN: Adding Control to Financial Market Data Generation with Semantic Context
  Haochong Xia, Shuo Sun, Xinrun Wang, Bo An
  AAAI, 2024.
  Paper | Codes

• RSAP-DFM: Regime-Shifting Adaptive Posterior Dynamic Factor Model for Stock Returns Prediction
  Quanzhou Xiang, Zhan Chen, Qi Sun, Rujun Jiang
  IJCAI, 2024.
  Paper

• Automatic De-Biased Temporal-Relational Modeling for Stock Investment Recommendation
  Weijun Chen, Shun Li, Xipu Yu, Heyuan Wang, Wei Chen, Tengjiao Wang
  IJCAI, 2024.
  Paper

• GENERATIVE LEARNING FOR FINANCIAL TIME SERIES WITH IRREGULAR AND SCALE-INVARIANT PATTERNS
  Hongbin Huang, Minghua Chen, Xiao Qiao
  ICLR, 2024.
  Paper

• DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation
  Yuan Gao, Haokun Chen, Xiang Wang, Zhicai Wang, Xue Wang, Jinyang Gao, Bolin Ding
  arXiv 2024.
  Paper

• FactorVAE: A Probabilistic Dynamic Factor Model Based on Variational Autoencoder for Predicting Cross-Sectional Stock Returns
  Yitong Duan, Lei Wang, Qizhong Zhang, Jian Li
  AAAI, 2022.
  Paper | Codes

G. Classical Time Series Models

• Learning connections in financial time series
  Ganeshapillai, Gartheeban; John Guttag; Andrew Lo
  ICML, 2013.
  Paper

H. Quantitative Open Sourced Framework

• RD-Agent: Autonomous evolving agents for industrial data-drive R&D
  Microsoft Research Asia
  arXiv 2024.
  Codes

• Qlib: An AI-oriented Quantitative Investment Platform
  Microsoft Research Asia
  arXiv 2021.
  Paper | Codes

I. Alpha Factor Mining

• AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factors
  Hao Shi, Weili Song, Xinting Zhang, Jiahe Shi, Cuicui Luo, Xiang Ao, Hamid Arian, Luis Seco
  AAAI, 2025.
  Paper | Codes

J. Survey

• From Deep Learning to LLMs: A survey of AI in Quantitative Investment
  Bokai Cao, Saizhuo Wang, Xinyi Lin, Xiaojun Wu, Haohan Zhang, Lionel M Ni, Jian Guo
  arXiv 2025.
  Paper

• Large Language Model Agent in Financial Trading: A Survey
  Han Ding, Yinheng Li, Junhao Wang, Hang Chen
  arXiv 2024.
  Paper

• Stock Market Prediction via Deep Learning Techniques: A Survey
  Jinan Zou, Qingying Zhao, Yang Jiao, Haiyao Cao, Yanxi Liu, Qingsen Yan, Ehsan Abbasnejad, Lingqiao Liu, Javen Qinfeng Shi
  arXiv 2023.
  Paper

• A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting
  Pierre-Daniel Arsenault, Shengrui Wang, Jean-Marc Patenaude
  ACM Computing Surveys, 2025.
  Paper

• Data-Driven Stock Forecasting Models Based on Neural Networks: A Review
  Wuzhida Bao, Yuting Cao, Yin Yang, Hangjun Che, Junjian Huang, Shiping Wen
  Information Fusion, 2025.
  Paper

• Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
  Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Yuyang Wang, Danielle Maddix, Caner Turkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, François-Xavier Aubet, Laurent Callot, Tim Januschowski
  ACM Computing Surveys, 2023.
  Paper

• Generative Adversarial Networks in Time Series: A Systematic Literature Review
  Eoin Brophy, Zhengwei Wang, Qi She, Tomás Ward
  ACM Computing Surveys, 2023.
  Paper

• Graph Neural Networks for Financial Fraud Detection: A Review
  Dawei Cheng, Yao Zou, Sheng Xiang, Changjun Jiang
  Frontiers of Computer Science, 2025.
  Paper

• Time Series Compression Survey
  Giacomo Chiarot, Claudio Silvestri
  ACM Computing Surveys, 2023.
  Paper

• LTSM-Bundle: A Toolbox and Benchmark on Large Language Models for Time Series Forecasting
  Yu-Neng Chuang, Songchen Li, Jiayi Yuan, Guanchu Wang, Kwei-Herng Lai, Songyuan Sui, Leisheng Yu, Sirui Ding, Chia-Yuan Chang, Qiaoyu Tan, Daochen Zha, Xia Hu
  arXiv, 2025.
  Paper

• Graph Deep Learning for Time Series Forecasting
  Andrea Cini, Ivan Marisca, Daniele Zambon, Cesare Alippi
  ACM Computing Surveys, 2025.
  Paper

• Empowering Time Series Analysis with Large Language Models: A Survey
  Yushan Jiang, Zijie Pan, Xikun Zhang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, Dongjin Song
  IJCAI, 2024.
  Paper

• Foundation Models for Time Series: A Survey
  arXiv 2025.
  Paper

• Harnessing Vision Models for Time Series Analysis: A Survey
  IJCAI 2025 (Survey Track).
  Paper

• Empowering Time Series Analysis with Synthetic Data: A Survey
  arXiv 2025.
  Paper

• A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models
  arXiv 2025.
  Paper

• FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting
  Yifan Hu, Yuante Li, Peiyuan Liu, Yuxia Zhu, Naiqi Li, Tao Dai, Shu-tao Xia, Dawei Cheng, Changjun Jiang
  Frontiers of Computer Science, 2026 (Best Paper, ICAIFW 2025).
  Paper | arXiv

• Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
  Ming Jin, Qingsong Wen, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, Xiaoli Li, Shirui Pan, Vincent S. Tseng, Yu Zheng, Lei Chen, Hui Xiong
  arXiv, 2023.
  Paper

• Foundation Models for Time Series Analysis: A Tutorial and Survey
  Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shirui Pan, Qingsong Wen
  SIGKDD, 2024.
  Paper

• Empowering Time Series Analysis with Foundation Models: A Comprehensive Survey
  Jiexia Ye, Yongzi Yu, Weiqi Zhang, Le Wang, Jia Li, Fugee Tsung
  arXiv 2025.
  Paper

• TSRBench: A Comprehensive Multi-task Multi-modal Time Series Reasoning Benchmark for Generalist Models
  arXiv 2026.
  Paper

📚 Citation

🎓 If you find this repository helpful for your research, please consider citing our work:

📋 BibTeX Citations

@inproceedings{li2025r,
  title={R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization},
  author={Li, Yuante and Yang, Xu and Yang, Xiao and Xu, Minrui and Wang, Xisen and Liu, Weiqing and Bian, Jiang},
  booktitle={NeurIPS},
  year={2025}
}

@article{zhu2025financial,
  title={Financial Time Series Prediction With Multi-Granularity Graph Augmented Learning}, 
  author={Zhu, Peng and Li, Yuante and Liu, Qinyuan and Cheng, Dawei and Jiang, Changjun},
  journal={IEEE Transactions on Knowledge and Data Engineering}, 
  year={2025},
}

@article{hu2026fintsb,
  title={FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting}, 
  author={Hu, Yifan and Li, Yuante and Liu, Peiyuan and Zhu, Yuxia and Li, Naiqi and Dai, Tao and Xia, Shu-tao and Cheng, Dawei and Jiang, Changjun},
  journal={Frontiers of Computer Science},
  year={2026},
  issn={2095-2228},
  doi={10.1007/s11704-026-51064-5}
}

@article{hu2025finmamba,
  title={FinMamba: Market-Aware Graph Enhanced Multi-Level Mamba for Stock Movement Prediction}, 
  author={Hu, Yifan and Liu, Peiyuan and Li, Yuante and Cheng, Dawei and Li, Naiqi and Dai, Tao and Bao, Jigang and Xia Shu-Tao},
  journal={arXiv preprint arXiv:2502.06707},
  year={2025}
}

@inproceedings{hu2025timefilter,
  title={TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting},
  author={Yifan Hu and Guibin Zhang and Peiyuan Liu and Disen Lan and Naiqi Li and Dawei Cheng and Tao Dai and Shu-Tao Xia and Shirui Pan},
  booktitle={ICML},
  year={2025}
}

@inproceedings{hu2025adaptive,
  title={Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting},
  author={Hu, Yifan and Liu, Peiyuan and Zhu, Peng and Cheng, Dawei and Dai, Tao},
  booktitle={AAAI},
  year={2025}
}

@inproceedings{bian2024multi,
  title={Multi-patch prediction: adapting language models for time series representation learning},
  author={Bian, Yuxuan and Ju, Xuan and Li, Jiangtong and Xu, Zhijian and Cheng, Dawei and Xu, Qiang},
  booktitle={ICML},
  year={2024}
}

@inproceedings{hu2026bridging,
  title={Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting},
  author={Hu, Yifan and Yang, Jie and Zhou, Tian and Liu, Peiyuan and Tang, Yujin and Jin, Rong and Sun, Liang},
  booktitle={ICLR},
  year={2026}
}

@inproceedings{liu2025timebridge,
  title={TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting},
  author={Liu, Peiyuan and Wu, Beiliang and Hu, Yifan and Li, Naiqi and Dai, Tao and Bao, Jigang and Xia, Shu-Tao},
  booktitle={ICML},
  year={2025}
}

@article{liu2025llm4fts,
  title={LLM4FTS: Enhancing Large Language Models for Financial Time Series Prediction},
  author={Liu, Zian and Jia, Renjun},
  journal={arXiv preprint arXiv:2505.02880},
  year={2025}
}

@article{yu2026peakfocus,
  title={PeakFocus: Bridging Peak Localization and Intensity Regression via a Unified Multi-Scale Framework for Electricity Load Forecasting},
  author={Yu, Wangzhi and Zhu, Peng and Zhao, Qing and Jiang, Yiwen and Cheng, Dawei},
  journal={arXiv preprint arXiv:2605.21550},
  year={2026}
}

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👥 Contributors

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