Abstract
Time series data mining techniques have attracted extensive attention from researchers worldwide. Of these techniques, time series classification is an important part of time series mining. Among the many time series classification algorithms, methods based on the bag-of-patterns algorithm have attracted much attention from researchers because of their high accuracy and execution efficiency. However, when using these methods, only the frequency of different patterns is considered. Features such as the position of patterns in a sequence are not mined. Therefore, the aim of this paper is to determine how to solve the problem that the positional relationships among patterns are ignored when using the bag-of-patterns algorithm. To solve this issue, we introduce the graph embedding technique, and an attempt is made to capture the positional relationships among the patterns of time series from the graph perspective. To verify the performance of the method, we perform extensive experiments with the UCR time series archive, and the experimental results demonstrate that our proposed method generally improves the classification ability of models based on the bag-of-patterns algorithm.
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Data availability
The datasets used in our study are available in the UCR repository, https://www.cs.ucr.edu/ eamonn/time_series_data_ 2018/.
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Funding
This work is partially supported by the National Natural Science Foundation of China Grant Number 61972424, in part by JSPS KAKENHI Grant Numbers JP19K20250,JP20H04174,JP22K11989, Leading Initiative for Excellent Young Researchers (LEADER), MEXT, Japan, and JST, PRESTO Grant Number JPMJPR21P3, Japan. The authors thank all the anonymous reviewers for their valuable comments.
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Ma, X., Yu, M., Huang, H. et al. A time series classification method combining graph embedding and the bag-of-patterns algorithm. Appl Intell 53, 26297–26312 (2023). https://doi.org/10.1007/s10489-023-04859-z
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DOI: https://doi.org/10.1007/s10489-023-04859-z