Time series shapelets are subsequences that best split time series data into classes. Therefore, shapelet discovery has attracted considerable interest in the time series classification community. However, almost all state-of-the-art shapelet-based time series classification methods have an inevitably high computational cost. To overcome this drawback, we present a regularized shapelet learning framework in which the fused lasso regularizer is used to maintain the time order of the shapelets and different loss functions can be employed to improve the speed and accuracy of the time series classification. The proposed framework converts the traditional brute force shapelet searching process into a regularized machine learning problem. The most prominent advantage of this conversion is that the speed of the shapelet learning process and the discrimination of the learned shapelets are both theoretically guaranteed. As such, both the speed and accuracy of the shapelet-based time series classification are improved in this paper. Comparison experiments on several datasets show that our framework effectively reduces the training time of time series classification while improving the classification accuracy.

时间序列shapelet是将时间序列数据最好地分成几类的子序列。因此，小波发现在时间序列分类界引起了极大的兴趣。但是，几乎所有基于Shapelet的最新时间序列分类方法都不可避免地具有很高的计算成本。为克服此缺点，我们提出了一种正则化的小波学习框架，其中融合的套索正则化器用于维持小波的时间顺序，并且可以采用不同的损失函数来提高时间序列分类的速度和准确性。提出的框架将传统的蛮力小形搜索过程转换为规则化的机器学习问题。这种转换的最显着优势是，理论上保证了小波学习过程的速度和所学小波的区分。这样，本文改进了基于小波的时间序列分类的速度和准确性。在多个数据集上的比较实验表明，我们的框架有效地减少了时间序列分类的训练时间，同时提高了分类精度。