XG-SF: An XGBoost Classifier Based on Shapelet Features for Time Series Classification

摘要

Time series classification (TSC) has attracted significant interest over the past decade. A lot of TSC methods have been proposed. Among these TSC methods, shapelet based methods are promising for they are interpretable, more accurate, and faster than other methods. For this, a lot of acceleration strategies are proposed. However, the accuracies of speedup methods are not ideal. To address these problems, an XGBoost classifier based on shapelet features (XG-SF) is proposed in this work. In XG-SF, an XGBoost classifier based on shapelet features is used to improve classification accuracy. Our experimental results demonstrate that XG-SF is faster than the state-of-the-art classifiers and the classification accuracy rate is also improved to a certain extent.