| Time-llm: Time series forecasting by reprogramming large language models M Jin, S Wang, L Ma, Z Chu, JY Zhang, X Shi, PY Chen, Y Liang, YF Li, ... 2024 Twelfth International Conference on Learning Representations (ICLR), 2023 | 313 | 2023 |
| A spatial–temporal attention approach for traffic prediction X Shi, H Qi, Y Shen, G Wu, B Yin IEEE Transactions on Intelligent Transportation Systems 22 (8), 4909-4918, 2020 | 175 | 2020 |
| Timemixer: Decomposable multiscale mixing for time series forecasting S Wang, H Wu, X Shi, T Hu, H Luo, L Ma, JY Zhang, J Zhou 2024 Twelfth International Conference on Learning Representations (ICLR), 2024 | 80 | 2024 |
| A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud S Xue, C Qu, X Shi, C Liao, S Zhu, X Tan, L Ma, S Wang, S Wang, Y Hu, ... Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 40 | 2022 |
| Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning X Shi, S Xue, K Wang, F Zhou, JY Zhang, J Zhou, C Tan, H Mei Advances in Neural Information Processing Systems (NeurIPS 2023), 2023 | 34 | 2023 |
| Easytpp: Towards open benchmarking the temporal point processes S Xue, X Shi, Z Chu, Y Wang, F Zhou, H Hao, C Jiang, C Pan, Y Xu, ... 2024 Twelfth International Conference on Learning Representations (ICLR), 2023 | 32 | 2023 |
| HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences S Xue, X Shi, JY Zhang, H Mei Advances in Neural Information Processing Systems 35 (2022): 34641-34650, 2022 | 30 | 2022 |
| Prompt-augmented temporal point process for streaming event sequence S Xue, Y Wang, Z Chu, X Shi, C Jiang, H Hao, G Jiang, X Feng, JY Zhang, ... Advances in Neural Information Processing Systems (NeurIPS 2023), 2023 | 15 | 2023 |
| Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes C Qu, X Tan, S Xue, X Shi, J Zhang, H Mei In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37 …, 2022 | 14 | 2022 |
| A graph regularized point process model for event propagation sequence S Xue, X Shi, H Hao, L Ma, J Zhang, S Wang, S Wang 2021 International Joint Conference on Neural Networks (IJCNN), 1-7, 2021 | 11 | 2021 |
| Full Scaling Automation for Sustainable Development of Green Data Centers S Wang, Y Sun, X Shi, S Zhu, LT Ma, J Zhang, Y Zheng, J Liu Proceedings of the Thirty-Second International Joint Conference on …, 2023 | 6 | 2023 |
| Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts X Shi, S Wang, Y Nie, D Li, Z Ye, Q Wen, M Jin arXiv preprint arXiv:2409.16040, 2024 | 2 | 2024 |
| Adaptive Learning on User Segmentation: Universal to Specific Representation via Bipartite Neural Interaction X Tan, Y Deng, C Qu, S Xue, X Shi, J Zhang, X Qiu Proceedings of the Annual International ACM SIGIR Conference on Research and …, 2023 | 1 | 2023 |
| TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis S Wang, J Li, X Shi, Z Ye, B Mo, W Lin, S Ju, Z Chu, M Jin arXiv preprint arXiv:2410.16032, 2024 | | 2024 |
| Computing resource configuration methods and apparatuses S Xue, X Shi, C Liao, S Zhu, J Li, Y Zheng, HU Yun, L Lei US Patent App. 18/450,036, 2024 | | 2024 |
| Scaling to Billion Parameters for Time Series Foundation Models with Mixture of Experts X Shi, S Wang, Y Nie, D Li, Z Ye, Q Wen, M Jin NeurIPS Workshop on Time Series in the Age of Large Models, 0 | | |
