Abstract: For time series classification,the method of classification by extracting the shapelets of time series and has attracted widespread attention due to its high classification accuracy and good interpretability.Most of the existing shapelets-based me-thods learn the shared shapelets for all classes,which can distinguish most classes,but not the unique class.Besides,the shapelets obtained by those models using adversarial strategies have problems like insufficient diversity.In order to solve these problems,this paper proposes a category-specific and diverse shapelets extraction method based on adversarial strategies.This method embeds the category information into the time series,adversarially generates a number of different category-specific shapelets by using the multi-generator module.The diversity of shapelets are guaranteed by imposing a difference constraint,and the last step uses the features obtained by the shapelets transformation to classify the time series.The proposed method is experimentally compared with 5 shapelets-based algorithms and 11 state-of-the-art classification algorithms on 36 time-series datasets.Experimental results show that,compared with 5 shapelets-based algorithms and 11 advanced classification algorithms,the proposed method achieves the best results on 26 and 20 datasets out of 36 datasets,and both achieve the highest average ranks,and its ave-rage classification accuracy is 2.4% higher than other methods at least,and 20% higher at most.Ablation analysis and visualization analysis demonstrate the effectiveness of diversity and category-specific approaches to time series classification.

Key words: Time series, shapelets, Category-specific, Diversity, Adversarial networks