Study on Time Series Shapelets Extraction Based on Optimization and Two-phase Filtering

Abstract

Compared with the time series classification methods based on global features,the shapelet-based methods have more advantages in interpretability,efficiency and accuracy.In order to solve the problems of insufficient discrimination of shapelets obtained from existing sparse models and the large scale of shapelets candidates,this paper proposes a shapelets extraction method based on optimization and two-phase filtering.First,the time series are sampled,and the sampled time series are grouped by combining the extreme points and the trend,then the weight of each item in the sparse group lasso regularizer are assigned according to the grouping results.The fused penalty regularization is used in each group of the weighted sparse group lasso to ensure that the adjacent positions of the solution change smoothly.Those sparse regularization terms are combined as constraints to construct the objective function together with the local fisher discriminant analysis.Then,a two-phase filtering framework is established to measure the sparsity of groups,so as to quickly locate the key group that plays a decisive role in classification.Finally,this key group is retained to extract shapelets for time series classification,which reduces the candidates of shapelets.Extensive experiments are carried out on 28 datasets,and the experimental results show that,compared with the existing shapelets-based extraction methods,the proposed method significantly improves the classification accuracy with a good efficiency,and reduces the scale of shapelets to a certain extent.

Keywords

• shapelets|two-phase filtering framework|weighted sparse group lasso|fused penalty|key group