Using Empirical Recurrence Rates Ratio for Time Series Data Similarity
Using Empirical Recurrence Rates Ratio for Time Series Data Similarity
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DOI：
10.1109/access.2018.2837660
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发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Zhan, Justin
中科院分区：
文献类型：
--
作者：
Bhaduri, Moinak;Zhan, Justin
在分类文献中存在几种方法来量化两个时间序列数据集之间的相似性。这些方法的应用范围从传统的欧几里得型度量到更先进的动态时间规整度量。这些工具中的大多数都充分解决了结构相似性问题，但无法满足结构相似性之外的目标。例如，一个可以很好地识别两个时间序列向量之间的季节相似性的工具可能在存在离群值的情况下证明是不够的。在本文中，我们提出了一个统一的措施，执行良好的二进制分类，同时包括几个方面的不同。该统计量在地质学和金融学等各个领域越来越突出，并且在时间序列数据库形成和聚类研究中至关重要。
Several methods exist in classification literature to quantify the similarity between two time series data sets. Applications of these methods range from the traditional Euclidean-type metric to the more advanced Dynamic Time Warping metric. Most of these adequately address structural similarity but fail in meeting goals outside it. For example, a tool that could be excellent to identify the seasonal similarity between two time series vectors might prove inadequate in the presence of outliers. In this paper, we have proposed a unifying measure for binary classification that performed well while embracing several aspects of dissimilarity. This statistic is gaining prominence in various fields, such as geology and finance, and is crucial in time series database formation and clustering studies.