Ming Jin

According to our database, Ming Jin authored at least 37 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection.

Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, Cesare Alippi, Geoffrey I. Webb, Irwin King, Shirui Pan

IEEE Trans. Pattern Anal. Mach. Intell., December, 2024

Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects.

Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Y. Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, Shirui Pan

IEEE Trans. Pattern Anal. Mach. Intell., October, 2024

Correlation-Aware Spatial-Temporal Graph Learning for Multivariate Time-Series Anomaly Detection.

Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, Khoa Tran Phan, Shirui Pan, Yi-Ping Phoebe Chen, Wei Xiang

IEEE Trans. Neural Networks Learn. Syst., September, 2024

Toward Graph Self-Supervised Learning With Contrastive Adjusted Zooming.

Yizhen Zheng, Ming Jin, Shirui Pan, Yuan-Fang Li, Hao Peng, Ming Li, Zhao Li

IEEE Trans. Neural Networks Learn. Syst., July, 2024

Towards complex dynamic physics system simulation with graph neural ordinary equations.

Guangsi Shi, Daokun Zhang, Ming Jin, Shirui Pan, Philip S. Yu

Neural Networks, 2024

Rethinking self-supervised learning for time series forecasting: A temporal perspective.

Shubao Zhao, Xinxing Zhou, Ming Jin, Zhaoxiang Hou, Chengyi Yang, Zengxiang Li, Qingsong Wen, Yi Wang, Yanlong Wen, Xiaojie Yuan

Knowl. Based Syst., 2024

Graph spatiotemporal process for multivariate time series anomaly detection with missing values.

Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, Haishuai Wang, Khoa Tran Phan, Yi-Ping Phoebe Chen, Shirui Pan, Wei Xiang

Inf. Fusion, 2024

TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis.

Shiyu Wang, Jiawei Li, Xiaoming Shi, Zhou Ye, Baichuan Mo, Wenze Lin, Shengtong Ju, Zhixuan Chu, Ming Jin

CoRR, 2024

Towards Neural Scaling Laws for Time Series Foundation Models.

Qingren Yao, Chao-Han Huck Yang, Renhe Jiang, Yuxuan Liang, Ming Jin, Shirui Pan

CoRR, 2024

Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts.

Xiaoming Shi, Shiyu Wang, Yuqi Nie, Dianqi Li, Zhou Ye, Qingsong Wen, Ming Jin

CoRR, 2024

Towards Universal Large-Scale Foundational Model for Natural Gas Demand Forecasting.

Xinxing Zhou, Jiaqi Ye, Shubao Zhao, Ming Jin, Zhaoxiang Hou, Chengyi Yang, Zengxiang Li, Yanlong Wen, Xiaojie Yuan

CoRR, 2024

DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs.

Dongyuan Li, Shiyin Tan, Ying Zhang, Ming Jin, Shirui Pan, Manabu Okumura, Renhe Jiang

CoRR, 2024

A Survey on Diffusion Models for Time Series and Spatio-Temporal Data.

Yiyuan Yang, Ming Jin, Haomin Wen, Chaoli Zhang, Yuxuan Liang, Lintao Ma, Yi Wang, Chenghao Liu, Bin Yang, Zenglin Xu, Jiang Bian, Shirui Pan, Qingsong Wen

CoRR, 2024

Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective.

Jiaxi Hu, Yuehong Hu, Wei Chen, Ming Jin, Shirui Pan, Qingsong Wen, Yuxuan Liang

CoRR, 2024

Position Paper: What Can Large Language Models Tell Us about Time Series Analysis.

Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, Qingsong Wen

CoRR, 2024

HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling for Long-Term Forecasting.

Shubao Zhao, Ming Jin, Zhaoxiang Hou, Chengyi Yang, Zengxiang Li, Qingsong Wen, Yi Wang

CoRR, 2024

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

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Position: What Can Large Language Models Tell Us about Time Series Analysis.

Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, Qingsong Wen

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting.

Shubao Zhao, Ming Jin, Zhaoxiang Hou, Chengyi Yang, Zengxiang Li, Qingsong Wen, Yi Wang

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

2023

Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection.

Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa Tran Phan, Yi-Ping Phoebe Chen

IEEE Trans. Knowl. Data Eng., December, 2023

Multivariate Time Series Forecasting With Dynamic Graph Neural ODEs.

Ming Jin, Yu Zheng, Yuan-Fang Li, Siheng Chen, Bin Yang, Shirui Pan

IEEE Trans. Knowl. Data Eng., September, 2023

Graph Self-Supervised Learning: A Survey.

Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, Philip S. Yu

IEEE Trans. Knowl. Data Eng., June, 2023

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

CoRR, 2023

Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects.

Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, Shirui Pan

CoRR, 2023

Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs.

Guangsi Shi, Daokun Zhang, Ming Jin, Shirui Pan

CoRR, 2023

How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?

Ming Jin, Guangsi Shi, Yuan-Fang Li, Qingsong Wen, Bo Xiong, Tian Zhou, Shirui Pan

CoRR, 2023

Geometric Relational Embeddings: A Survey.

Bo Xiong, Mojtaba Nayyeri, Ming Jin, Yunjie He, Michael Cochez, Shirui Pan, Steffen Staab

CoRR, 2023

2022

From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach.

Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa Tran Phan, Shirui Pan, Yi-Ping Phoebe Chen

CoRR, 2022

Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs.

Ming Jin, Yuan-Fang Li, Shirui Pan

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming.

Yizhen Zheng, Ming Jin, Shirui Pan, Yuan-Fang Li, Hao Peng, Ming Li, Zhao Li

CoRR, 2021

Graph Self-Supervised Learning: A Survey.

Yixin Liu, Shirui Pan, Ming Jin, Chuan Zhou, Feng Xia, Philip S. Yu

CoRR, 2021

Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning.

Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, Shirui Pan

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning.

Ming Jin, Yixin Liu, Yu Zheng, Lianhua Chi, Yuan-Fang Li, Shirui Pan

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

2020

Optimized Coefficient Vector and Sparse Representation-Based Classification Method for Face Recognition.

Shigang Liu, Lingjun Li, Ming Jin, Sujuan Hou, Yali Peng

IEEE Access, 2020

Searching Correlated Patterns From Graph Streams.

Ming Jin, Mei Li, Yu Zheng, Lianhua Chi

IEEE Access, 2020

2019

A Clickthrough Rate Prediction Algorithm Based on Users' Behaviors.

Xi Xiong, Chuan Xie, Rongmei Zhao, Yuanyuan Li, Shenggen Ju, Ming Jin

IEEE Access, 2019