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 ...
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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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