基于多维Shapelets的时间序列数据动态分类方法

Yan Wang, Lingling Tian, Di Liu, Aiping Tan, Jiaqi Hu
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引用次数: 0

摘要

时间序列数据分类在文化科技融合企业财务分析中起着至关重要的作用。但是,目前对时间序列数据的分类主要集中在单一维度上,没有充分考虑时间序列数据的动态性,导致分类结果不准确。针对以上不足,对多维时间序列进行了研究,定义了shapelets单元和维度相关性,提出了一种基于多维shapelets的时间序列数据动态分类方法。该算法从样本的多个维度中筛选出生成shapelets候选集的最优时间序列,计算相关系数来衡量维度之间的相关性,并用于构建shapelets单元;动态更新关键参数,完成双判别分类算法进行分类操作。在论文的最后,基于多个数据集进行了实验。实验表明,即使在小样本量下,本文设计的算法准确率也能达到60%以上。此外,与现有的分类算法相比,对具有多维时间序列特征的目标的分类精度提高了至少8%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Dynamic Classification Method of Time-Series Data Based on Multidimensional Shapelets
Time series data classification plays a vital role in the financial analysis of cultural and technological integration enterprises. Still, the current classification of time series data mainly focuses on a single dimension and does not fully consider the dynamics of time series data, resulting in inaccurate classification results. Given the above shortcomings, multidimensional time series are studied, shapelets unit and dimension correlation are defined, and a dynamic classification method of time series data based on multi-dimensional shapelets is proposed. The algorithm screens out the optimal time series for generating shapelets candidate sets from multiple dimensions of the sample, and calculates the correlation coefficient to measure the correlation between the dimensions, and is used to construct the shapelets unit; it is also designed to dynamically update the key parameters to complete double discriminant classification algorithm for classification operation. At the end of the paper, experiments are conducted based on multiple data sets. The experiments show that even in a small sample size, the accuracy of the algorithm designed in this article can reach more than 60%. Furthermore, compared with existing classification algorithms, the classification accuracy of objects with multi-dimensional time-series features is improved by at least 8%.
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