金明｜成都信息工程大学｜学者主页

2026

Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecasting

Zhiqing Cui, Si-Ru Zhong, Ming Jin, Shi-Rui Pan, Qing-Song Wen, Yuxuan Liang

arXiv.org

2026

TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning

Ya-Xuan Kong, Qingren Yao, Yuqi Nie, Yichen Li, Yi Shao, Stefan Zohren, Anna Vettoruzzo, J. Vanschoren, Ming Jin, Qingsong Wen

arXiv.org

DOI

2026

It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks

Zhong-Zheng Qiao, Sheng Pan, Annie Wang, V. Zhukova, Yong Liu, Xudong Jiang, Qingsong Wen, Ming-Sheng Long, Ming Jin, Cheng-Hao Liu

arXiv.org

DOI

2026

Aionoscope: Debugging Latent-State Accessibility in Time-Series Representations

Alexander Chemeris, Ming Jin, Randall Balestriero

arXiv.org

DOI

2026

TimeSage-EV: A Live Benchmark for Agentic Time Series Analysis in Evolving Environments

Qingren Yao, Ya-Xuan Kong, Yuqi Nie, Yichen Li, Stefan Zohren, Anna Vettoruzzo, Qing-Song Wen, Ming Jin, J. Vanschoren

2026

Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databases

Zhao Tan, Yi-Ji Zhao, Shi-Yu Wang, Chang Xu, Yuxuan Liang, Xi-Ping Liu, Shi-Rui Pan, Ming Jin

arXiv.org

DOI

2026

STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning

Juntong Ni, Shiyu Wang, Ming Jin, Qi He, Wei Jin

Annual Meeting of the Association for Computational Linguistics

DOI

2026

Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks

Bo-Han Wang, Ze-Wen Liu, Lu Lin, Hui Liu, Li Xiong, Ming Jin, Wei Jin

arXiv.org

DOI

2026

FaST: Efficient and Effective Long-Horizon Forecasting for Large-Scale Spatial-Temporal Graphs via Mixture-of-Experts

Yiji Zhao, Zi-Hao Zhong, Ao Wang, Hao-Min Wen, Ming Jin, Yuxuan Liang, Huaiyu Wan, Hao Wu

Knowledge Discovery and Data Mining

DOI

2026

EventCast: Hybrid Demand Forecasting in E-Commerce with LLM-Based Event Knowledge

Congcong Hu, Yuan Shi, Fan Huang, Yang Xiang, Zhou Ye, Ming Jin, Shiyu Wang

arXiv.org

DOI

2026

TimeOmni-VL: Unified Models for Time Series Understanding and Generation

Tong Guan, Sheng Pan, Johan Barthelemy, Zhao Li, Yu-Jun Cai, C. Alippi, Ming Jin, Shi-Rui Pan

arXiv.org

DOI

2025

The Procrustean Bed of Time Series: The Optimization Bias of Point-wise Loss

Rong-Yao Cai, Yu-Xiang Wan, Ke-Xin Zhang, Ming Jin, Hao Wang, Zhi-Qiang Ge, Daoyi Dong, Yong Li, Qing-Song Wen

arXiv.org

DOI

2025

Source-Free Time-Series Domain Adaptation With Prior Evaluation of Model Salience

Rongyao Cai, Ming Jin, Qingsong Wen, Ke-Xin Zhang, Yong Liu

IEEE Transactions on Neural Networks and Learning Systems

DOI

2025

Multi-Scale Finetuning for Encoder-based Time Series Foundation Models

Zhong-Zheng Qiao, Chenghao Liu, Yiming Zhang, Ming Jin, Quang Pham, Qingsong Wen, P. Suganthan, Xudong Jiang, Savitha Ramasamy

Neural Information Processing Systems

DOI

2025

Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluation

Ya-Xuan Kong, Yi-Yuan Yang, Shiyu Wang, Cheng-Hao Liu, Yuxuan Liang, Ming Jin, Stefan Zohren, Dan Pei, Yan Liu, Qingsong Wen

2025

ShapeX: Shapelet-Driven Post Hoc Explanations for Time Series Classification Models

Bo-Song Huang, Ming Jin, Yuxuan Liang, Johan Barthelemy, De-Bo Cheng, Qingsong Wen, Cheng-Hao Liu, Shi-Rui Pan

Neural Information Processing Systems

DOI

2025

Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement

Ya-Xuan Kong, Yi-Yuan Yang, Yoontae Hwang, Wenjie Du, Stefan Zohren, Zhangyang Wang, Ming Jin, Qingsong Wen

Annual Meeting of the Association for Computational Linguistics

DOI

2025

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Si-Ru Zhong, Wei-Lin Ruan, Ming Jin, Huan Li, Qingsong Wen, Yuxuan Liang

International Conference on Machine Learning

DOI

2025

Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey

Yuxuan Liang, Hao-Min Wen, Yutong Xia, Ming Jin, Bin Yang, F. Salim, Qingsong Wen, Shi-Rui Pan, Gao Cong

Knowledge Discovery and Data Mining

DOI

2025

SCOPE-MoE: Supply Chain Forecasting with a Pretrained MoE-Based Large Time Series Model in E-Commerce

Shiyu Wang, Xinyue Zhong, Jiawei Li, Bai-Chuan Mo, Zhou Ye, Ming Jin

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)

DOI

2025

TimeDistill: Efficient Long-Term Time Series Forecasting with MLP via Cross-Architecture Distillation

Juntong Ni, Ze-Wen Liu, Shiyu Wang, Ming Jin, Wei Jin

Knowledge Discovery and Data Mining

DOI

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, Qing-Song Wen, Yi Wang

International Conference on Information and Knowledge Management

DOI

2024

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

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

International Conference on Learning Representations

DOI

2024

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

Yi-Yuan Yang, Ming Jin, Hao-Min Wen, Chao-Li Zhang, Yuxuan Liang, Lin-Tao Ma, Yi Wang, Cheng-Hao Liu, Bin Yang, Zeng-Lin Xu, Jiang Bian, Shi-Rui Pan, Qing-Song Wen

ACM Computing Surveys

DOI

2024

Foundation Models for Time Series Analysis: A Tutorial and Survey

Yuxuan Liang, Hao-Min Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shi-Rui Pan, Qing-Song Wen

Knowledge Discovery and Data Mining

DOI

2024

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

Ming Jin, Yifan Zhang, Wei Chen, Ke-Xin Zhang, Yuxuan Liang, Bin Yang, Jin-Dong Wang, Shi-Rui Pan, Qingsong Wen

International Conference on Machine Learning

2024

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

Jia-Xi Hu, Yuehong Hu, Wei Chen, Ming Jin, Shi-Rui Pan, Qing-Song Wen, Yuxuan Liang

Neural Information Processing Systems

DOI

2024

Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values

Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, Hai-Shuai Wang, K. Phan, Yi-ping Phoebe Chen, Shi-Rui Pan, Wei Xiang

Information Fusion

DOI

2024

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

Shiyu Wang, Jia-Wei Li, Xiao-Ming Shi, Zhou Ye, Bai-Chuan Mo, Wen-Ze Lin, Shengtong Ju, Zhixuan Chu, Ming Jin

International Conference on Learning Representations

DOI

2023

Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting

Ming Jin, Guang-Si Shi, Yuan-Fang Li, Bo Xiong, Tian Zhou, F. Salim, Liang Zhao, Ling-Fei Wu, Qingsong Wen, Shirui Pan

IEEE Transactions on Pattern Analysis and Machine Intelligence

DOI

2023

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Ming Jin, Shiyu Wang, Lin-Tao Ma, Zhixuan Chu, James Y. Zhang, X. Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shi-Rui Pan, Qing-Song Wen

International Conference on Learning Representations

DOI

2023

Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook

Ming Jin, Ya-Xuan Kong, Yuxuan Liang, Chao-Li Zhang, Si-Qiao Xue, Xue Wang, James Y. Zhang, Yi Wang, Hai-Feng Chen, Xiao-Li Li, Vincent Tseng, Yu Zheng, Lei Chen, Hui Xiong, Shi-Rui Pan, Qing-Song Wen

ACM Computing Surveys

DOI

2023

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

Kexin Zhang, Qingsong Wen, Chao-Li Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Zhang, Y. Liang, Guansong Pang, Dong-Jin Song, Shirui Pan

IEEE Transactions on Pattern Analysis and Machine Intelligence

DOI

2023

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

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

IEEE Transactions on Pattern Analysis and Machine Intelligence

DOI

2023

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

Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, K. Phan, Shirui Pan, Yi-ping Phoebe Chen, Wei Xiang

IEEE Transactions on Neural Networks and Learning Systems

DOI

2022

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

Yu Zheng, Jun-Jun Pan, Yue Tan, Ming Jin, Yixin Liu, Lianhua Chi, K. Phan, Yi-Ping Phoebe Chen

Transactions on Graph Intelligence and Network Applications

DOI

2022

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

Ming Jin, Yuan-Fang Li, Shirui Pan

Neural Information Processing Systems

DOI

2022

Multivariate Time Series Forecasting With Dynamic Graph Neural ODEs

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

IEEE Transactions on Knowledge and Data Engineering

DOI

2021

ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning

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

International Conference on Information and Knowledge Management

DOI

2021

Toward Graph Self-Supervised Learning With Contrastive Adjusted Zooming

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

IEEE Transactions on Neural Networks and Learning Systems

DOI

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

International Joint Conference on Artificial Intelligence

DOI

2021

Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection

Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, K. Phan, Yi-Ping Phoebe Chen

IEEE Transactions on Knowledge and Data Engineering

DOI

2021

Graph Self-Supervised Learning: A Survey

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

IEEE Transactions on Knowledge and Data Engineering

DOI

2017

产业结构调整与就业结构变化关系的研究——以四川省为例

金明，霍晓荣，胡晓坤

《调研世界》，No. 3

下载原文

2015

四川省三次产业结构分析及优化

金明，刘子琳

《统计与决策》，No. 18

下载原文

2007

关于四川省和谐满意度评价的因子与聚类分析

胥健，范乔希，金明

《成都信息工程大学学报》

精选作品

期刊论文

Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecasting

TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning

It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks

Aionoscope: Debugging Latent-State Accessibility in Time-Series Representations

TimeSage-EV: A Live Benchmark for Agentic Time Series Analysis in Evolving Environments

Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databases

STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning

Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks

FaST: Efficient and Effective Long-Horizon Forecasting for Large-Scale Spatial-Temporal Graphs via Mixture-of-Experts

EventCast: Hybrid Demand Forecasting in E-Commerce with LLM-Based Event Knowledge

TimeOmni-VL: Unified Models for Time Series Understanding and Generation

The Procrustean Bed of Time Series: The Optimization Bias of Point-wise Loss

Source-Free Time-Series Domain Adaptation With Prior Evaluation of Model Salience

Multi-Scale Finetuning for Encoder-based Time Series Foundation Models

Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluation

ShapeX: Shapelet-Driven Post Hoc Explanations for Time Series Classification Models

Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement

Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting

Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey

SCOPE-MoE: Supply Chain Forecasting with a Pretrained MoE-Based Large Time Series Model in E-Commerce

TimeDistill: Efficient Long-Term Time Series Forecasting with MLP via Cross-Architecture Distillation

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

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

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

Foundation Models for Time Series Analysis: A Tutorial and Survey

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

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

Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values

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

Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook

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

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

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

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

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

Multivariate Time Series Forecasting With Dynamic Graph Neural ODEs

ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning

Toward Graph Self-Supervised Learning With Contrastive Adjusted Zooming

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

Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection

Graph Self-Supervised Learning: A Survey

产业结构调整与就业结构变化关系的研究——以四川省为例

四川省三次产业结构分析及优化

关于四川省和谐满意度评价的因子与聚类分析