Clustering of time series is a well-studied problem, with applications ranging from quantitative, personalized models of metabolism obtained from metabolite concentrations to state discrimination in quantum information theory. We consider a variant, where given a set of trajectories and a number of parts, we jointly partition the set of trajectories and learn linear dynamical system (LDS) models for each part, so as to minimize the maximum error across all the models. We present globally convergent methods and EM heuristics, accompanied by promising computational results.

翻译：时间序列聚类是一个研究充分的问题，其应用范围涵盖从基于代谢物浓度获得的定量个性化代谢模型，到量子信息理论中的状态区分。我们考虑一个变体问题：给定一组轨迹和若干部分，我们联合划分轨迹集合并为每个部分学习线性动力系统（LDS）模型，以最小化所有模型的最大误差。我们提出了全局收敛方法和EM启发式算法，并附有具有前景的计算结果。