Here we introduce Local Topological Recurrence Analysis (LoTRA), a simple computational approach for analyzing time-series data. Its versatility is elucidated using simulated data, Parkinsonian gait, and in vivo brain dynamics. We also show that this algorithm can be used to build a remarkably simple machine-learning model capable of outperforming deep-learning models in detecting Parkinson’s disease from a single digital handwriting test.

中文翻译：

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用于时间序列数据分析和机器学习模型的通用计算算法

在这里，我们介绍局部拓扑递归分析 (LoTRA)，这是一种用于分析时间序列数据的简单计算方法。使用模拟数据、帕金森步态和体内脑动力学阐明了其多功能性。我们还表明，该算法可用于构建一个非常简单的机器学习模型，该模型能够在通过单个数字笔迹测试检测帕金森病方面优于深度学习模型。