学术报告

题      目：Cluster analysis on wide-sense stationary ergodic processes and locally asymptotically self-similar processes 

报  告  人：饶楠  副教授  (邀请人：杨舟 )

                                    苏州大学

时      间：5月09日  11：00-12：00

地     点：数科院西楼二楼会议室

报告人简介:

        饶楠，苏州大学金融工程研究中心。毕业于美国Claremont Graduate University获得数学博士学位。曾于上海交通大学数学科学学院从事博士后研究。目前主要研究方向为分数随机过程、首达时问题、金融数学等方向。论文发表在《Machine Learning》、《Chaos, Fractals&Solitons》、《Fractal and Fractional》 等期刊上。

摘      要：

        We introduce a new unsupervised learning problem: clustering two different stochastic processes: wide-sense stationary ergodic processes and locally asymptotically self-similar processes. Covariance-based dissimilarity measures and asymptotically consistent algorithms are designed for clustering in offline and online data settings, respectively. We discuss an approach to improve the efficiency of clustering algorithms when they are applied to cluster self-similar processes. In a simulation study, several excellent examples are provided to show the efficiency and consistency of the clustering algorithms. Comparison to the other existing clustering algorithms is made. In a real world project, we successfully apply these algorithms to cluster the global equity markets of different regions.

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