A professionally curated list of Large (Language) Models and Foundation Models (LLM, LM, FM) for Temporal Data (Time Series, Spatiotemporal, and Event Data) and AIOps with awesome resources (paper, code, data, etc.), which aims to comprehensively and systematically summarize the recent advances to the best of our knowledge.
We will continue to update this list with newest resources. If you found any missed resources (paper/code) or errors, please feel free to open an issue or make a pull request.
For general AI for Time Series (AI4TS) Papers, Tutorials, and Surveys at the Top AI Conferences and Journals, please check This Repo.
- Voice2Series: Reprogramming Acoustic Models for Time Series Classification, in ICML 2021. [paper] [official code]
- One Fits All: Power General Time Series Analysis by Pretrained LM, in arXiv 2023. [paper]
- Large Language Models are Few-Shot Health Learners, in arXiv 2023. [paper]
- Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning, in NeurIPS 2023, [paper], [official-code]
- ClimaX: A foundation model for weather and climate, in ICML 2023. [paper] [official code]
- FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead, in arXiv 2023. [paper]
- Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast, in arXiv 2022. [paper]
- GraphCast: Learning skillful medium-range global weather forecasting, in arXiv 2022. [paper]
- FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operator, in arXiv 2022. [paper]
- Empowering Practical Root Cause Analysis by Large Language Models for Cloud Incidents, in arXiv 2023. [paper]
- Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models, in arXiv 2023. [paper]
- A Survey of Large Language Models, in arXiv 2023. [paper] [link]
- Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond, in arXiv 2023. [paper] [link]
- LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models, in arXiv 2023. [paper] [link]
- Beyond One-Model-Fits-All: A Survey of Domain Specialization for Large Language Models, in arXiv 2023. [paper]
- Large AI Models in Health Informatics: Applications, Challenges, and the Future, in arXiv 2023. [paper] [link]
- FinGPT: Open-Source Financial Large Language Models, in arXiv 2023. [paper] [link]
- On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence, in arXiv 2023. [paper]
- Awesome LLM. [link]
- Open LLMs. [link]
- Awesome LLMOps. [link]
- Awesome Foundation Models. [link]
- Awesome Graph LLM. [link]
- Awesome Multimodal Large Language Models. [link]
- Transformers in Time Series: A Survey, in IJCAI 2023. [paper] [GitHub Repo]
- Time series data augmentation for deep learning: a survey, in IJCAI 2021. [paper]
- Time-series forecasting with deep learning: a survey, in Philosophical Transactions of the Royal Society A 2021. [paper]
- A review on outlier/anomaly detection in time series data, in CSUR 2021. [paper]
- Deep learning for time series classification: a review, in Data Mining and Knowledge Discovery 2019. [paper]
- A Survey on Time-Series Pre-Trained Models, in arXiv 2023. [paper] [link]
- Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects, in arXiv 2023. [paper] [Website]
- A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection, in arXiv 2023. [paper] [Website]
- AIOps: real-world challenges and research innovations, in ICSE 2019. [paper]
- A Survey of AIOps Methods for Failure Management, in TIST 2021. [paper]
- AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges, in arXiv 2023. [paper]
@misc{wen2023llmfortimeseriesaiops,
title={Awesome-TimeSeries-AIOps-LM-LLM},
author={Wen, Qingsong},
journal = {GitHub repository},
year={2023}
}