论文标题
与拓扑数据分析的随机过程分类
Classification of Stochastic Processes with Topological Data Analysis
论文作者
论文摘要
在这项研究中，我们检查了工程拓扑特征是否可以区分平衡和不平衡采样方案中的噪声特征不同的随机过程。我们将分类结果与基于统计和原始功能构建的相同分类任务的结果进行比较。我们得出的结论是，在时间序列的分类任务中，建立在工程拓扑特征上的不同机器学习模型比在标准统计和原始功能上构建的拓扑功能始终如一。
In this study, we examine if engineered topological features can distinguish time series sampled from different stochastic processes with different noise characteristics, in both balanced and unbalanced sampling schemes. We compare our classification results against the results of the same classification tasks built on statistical and raw features. We conclude that in classification tasks of time series, different machine learning models built on engineered topological features perform consistently better than those built on standard statistical and raw features.
