报告题目：Efficient Online Prediction for High-Dimensional Time Series via Joint Tensor Tucker Decomposition

报告人：张立平教授（清华大学）

报告时间：2025年11月21日上午10：00-11：00

报告地点：数乳交 425报告厅

邀请人：白敏茹

报告摘要：Real-time prediction plays a vital role in various control systems, such as traffic congestion control and wireless channel resource allocation. In these scenarios, the predictor usually needs to track the evolution of the latent statistical patterns in the modern high-dimensional streaming time series continuously and quickly, which presents new challenges for traditional prediction methods. This paper proposes a novel algorithm based on tensor factorization to predict streaming tensor time series online. The proposed algorithm updates the predictor in a low-complexity online manner to adapt to the time-evolving data. Additionally, an automatically adaptive version of the algorithm is presented to mitigate the negative impact of stale data. Simulation results demonstrate that our proposed methods achieve prediction accuracy similar to that of conventional offline tensor prediction methods, while being much faster than them during long-term online prediction.

报告专家简介：张立平，清华大学长聘教授，博士生导师，研究方向最优化理论算法及应用，在求解互补与变分不等式问题、半无限规划、张量优化等方面取得了许多研究成果。已在Mathematical Programming, Mathematics of Computation, SIAM Journal of Matrix Analysis and Applications, SIAM Journal on Optimization, Mathematics of Operational Research, Science China Mathematic, Expert Systems with Applications等期刊发表论文多篇、连续获得多项国家自然科学基金资助。曾获得教育部自然科学奖二等奖和北京市科学技术奖二等奖。