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Time Series Classification Based on Fractal Properties

机译:基于分形特性的时间序列分类

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The article considers classification task of fractal time series by the meta algorithms based on decision trees. Binomial multiplicative stochastic cascades are used as input time series. Comparative analysis of the classification approaches based on different features is carried out. The results indicate the advantage of the machine learning methods over the traditional estimating the degree of self-similarity.
机译:该文章认为基于决策树的元算法分形时间序列的分类任务。二项式乘法随机级联级联用作输入时间序列。进行了基于不同特征的分类方法的比较分析。结果表明了机器学习方法在传统估计自相似度的程度上的优势。

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