/Awesome-LM4TS

Awesome Pre-trained Language Model for Time Series Analysis

Related Advancements

2024

2023

  1. 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.
  2. Cao D, Jia F, Arik S O, et al. TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting[J]. arXiv preprint arXiv:2310.04948, 2023.
  3. Garza A, Mergenthaler-Canseco M. TimeGPT-1[J]. arXiv preprint arXiv:2310.03589, 2023.
  4. Gruver N, Finzi M, Qiu S, et al. Large language models are zero-shot time series forecasters[J]. arXiv preprint arXiv:2310.07820, 2023.
  5. Liu X, McDuff D, Kovacs G, et al. Large Language Models are Few-Shot Health Learners[J]. arXiv preprint arXiv:2305.15525, 2023.
  6. Zhang B, Yang H, Liu X Y. Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models[J]. arXiv preprint arXiv:2306.12659, 2023.
  7. Jin, Ming, et al. "Time-LLM: Time Series Forecasting by Reprogramming Large Language Models." arXiv preprint arXiv:2310.01728 (2023).
  8. Zhou, Tian, et al. "One Fits All: Power General Time Series Analysis by Pretrained LM." arXiv preprint arXiv:2302.11939 (2023).
  9. Sun, Chenxi, et al. "TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series." arXiv preprint arXiv:2308.08241 (2023).
  10. Gruver, Nate, et al. "Large Language Models Are Zero-Shot Time Series Forecasters." arXiv preprint arXiv:2310.07820 (2023).
  11. Li, Jun, et al. "Frozen Language Model Helps ECG Zero-Shot Learning." arXiv preprint arXiv:2303.12311 (2023).
  12. Yu X, Chen Z, Ling Y, et al. Temporal Data Meets LLM--Explainable Financial Time Series Forecasting[J]. arXiv preprint arXiv:2306.11025, 2023.
  13. Xie Q, Han W, Lai Y, et al. The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges[J]. arXiv preprint arXiv:2304.05351, 2023.

2022~before

  1. Xue, Hao, and Flora D. Salim. "PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting." (2022).
  2. Li, Daoyuan, et al. "DSCo-NG: a practical language modeling approach for time series classification." Advances in Intelligent Data Analysis XV: 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings 15. Springer International Publishing, 2016.
  3. 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.

Survey

  1. Jin, Ming, et al. "Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook." arXiv preprint arXiv:2310.10196 (2023).