Research

My research spans a wide range of topics around (1) time series analysis, (2) graph neural networks, and (3) multimodal learning. I am particularly interested in (i) LLMs/FMs for temporal data, (ii) temporal data agents ,(iii) dynamic (e.g., spatio-temporal) graph neural networks, (iv) multimodal learning and modality switching.


Selected Publications

2024

  1. ICLR
    Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
    Jin Ming, Wang Shiyu, Ma Lintao, Chu Zhixuan, Zhang James Y, Shi Xiaoming, Chen Pin-Yu, Liang Yuxuan, Li Yuan-Fang, Pan Shirui,  others
    International Conference on Learning Representations
    arXiv Code Slides
  2. TPAMI
    Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
    Zhang Kexin, Wen Qingsong, Zhang Chaoli, Cai Rongyao,  Jin Ming, Liu Yong, Zhang James, Liang Yuxuan, Pang Guansong, Song Dongjin,  others
    IEEE Transactions on Pattern Analysis and Machine Intelligence
    arXiv Code
  3. Info. F.
    Graph spatiotemporal process for multivariate time series anomaly detection with missing values
    Zheng Yu, Koh Huan Yee,  Jin Ming, Chi Lianhua, Wang Haishuai, Phan Khoa T, Chen Yi-Ping Phoebe, Pan Shirui, Xiang Wei
    Information Fusion
    arXiv Code
  4. arXiv
    HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling for Long-Term Forecasting
    Zhao Shubao*,  Jin Ming*, Hou Zhaoxiang, Yang Chengyi, Li Zengxiang, Wen Qingsong, Wang Yi
    arXiv preprint
    arXiv
  5. arXiv
    What Can Large Language Models Tell Us about Time Series Analysis
    Jin Ming*, Zhang Yifan*, Chen Wei*, Zhang Kexin, Liang Yuxuan, Yang Bin, Wang Jindong, Pan Shirui, Wen Qingsong
    arXiv preprint
    arXiv
  6. arXiv
    Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective
    Hu Jiaxi, Hu Yuehong, Chen Wei,  Jin Ming, Pan Shirui, Wen Qingsong, Liang Yuxuan
    arXiv preprint
    arXiv

2023

  1. arXiv
    Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
    Jin Ming, Wen Qingsong, Liang Yuxuan, Zhang Chaoli, Xue Siqiao, Wang Xue, Zhang James Y., Wang Yi, Chen Haifeng, Li Xiaoli, Pan Shirui, Tseng Vincent S., Zheng Yu, Chen Lei, Xiong Hui
    arXiv preprint
    arXiv Code
  2. arXiv
    A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
    Jin Ming, Koh Huan Yee, Wen Qingsong, Zambon Daniele, Alippi Cesare, Webb Geoffrey I, King Irwin, Pan Shirui
    arXiv preprint
    arXiv Code
  3. TNNLS
    Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection
    Zheng Yu, Koh Huan Yee,  Jin Ming, Chi Lianhua, Phan Khoa T, Pan Shirui, Chen Yi-Ping Phoebe, Xiang Wei
    IEEE Transactions on Neural Networks and Learning Systems
    arXiv Code
  4. arXiv
    How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?
    Jin Ming, Shi Guangsi, Li Yuan-Fang, Wen Qingsong, Xiong Bo, Zhou Tian, Pan Shirui
    arXiv preprint
    arXiv
  5. arXiv
    WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine
    Xue Siqiao, Zhou Fan, Xu Yi,  Jin Ming, Wen Qingsong, Hao Hongyan, Dai Qingyang, Jiang Caigao, Zhao Hongyu, Xie Shuo, He Jianshan, Zhang James, Mei Hongyuan
    arXiv Code
  6. arXiv
    Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs
    Shi Guangsi, Zhang Daokun,  Jin Ming, Pan Shirui
    arXiv preprint
    arXiv
  7. arXiv
    Geometric Relational Embeddings: A Survey
    Xiong Bo, Nayyeri Mojtaba,  Jin Ming, He Yunjie, Cochez Michael, Pan Shirui, Staab Steffen
    arXiv preprint
    arXiv

2022

  1. NIPS
    Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs
    Jin Ming, Li Yuan-Fang, Pan Shirui
    The Conference on Neural Information Processing Systems
    PDF Code Poster Slides
  2. TKDE
    Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs
    Jin Ming, Zheng Yu, Li Yuan-Fang, Chen Siheng, Yang Bin, Pan Shirui
    IEEE Transactions on Knowledge and Data Engineering
    arXiv Code
  3. TKDE
    Graph Self-Supervised Learning: A Survey
    Liu Yixin*,  Jin Ming*, Pan Shirui, Zhou Chuan, Zheng Yu, Xia Feng, Yu Philip
    IEEE Transactions on Knowledge and Data Engineering
    arXiv
  4. TNNLS
    Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming
    Zheng Yizhen,  Jin Ming, Pan Shirui, Li Yuan-Fang, Peng Hao, Li Ming, Li Zhao
    IEEE Transactions on Neural Networks and Learning Systems
    arXiv

2021

  1. IJCAI
    Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
    Jin Ming, Zheng Yizhen, Li Yuan-Fang, Gong Chen, Zhou Chuan, Shirui Pan
    International Joint Conference on Artificial Intelligence
    arXiv Code Poster Slides
  2. CIKM
    ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning
    Jin Ming, Liu Yixin, Zheng Yu, Chi Lianhua, Li Yuan-Fang, Pan Shirui
    International Conference on Information & Knowledge Management
    PDF Code Poster Slides
  3. TKDE
    Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection
    Zheng Yu*,  Jin Ming*, Liu Yixin, Chi Lianhua, Phan Khoa T, Chen Yi-Ping Phoebe
    IEEE Transactions on Knowledge and Data Engineering
    arXiv Code