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缩放时间序列数据的指数:一种机器学习方法

Sebastian Raubitzek1,2, Luiza Corpaci3, Rebecca Hofer2

  • 1Information and Software Engineering Group, TU Wien, Favoritenstrasse 9-11/194, 1040 Vienna, Austria.

Entropy (Basel, Switzerland)
|December 23, 2023
PubMed
概括

机器学习模型准确地估计了赫斯特指数,在时间序列数据上表现优于传统方法,如重新缩放范围 (R/S) 分析和确定波动分析 (DFA). 这种新的方法增强了金融等领域的长期依赖性分析.

关键词:
赫斯特的指数是一个指数.人工智能的人工智能是人工智能.复杂性的复杂性 复杂性的复杂性机器学习是机器学习.回归分析是一种回归分析.扩展指数是一个扩展指数.

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