EffiShapeFormer: Shapelet-Based Sensor Time Series Classification with Dual Filtering and Convolutional-Inverted Attention

In the field of sensors, time series classification holds significant importance for applications such as industrial monitoring, mechanical fault diagnosis, and action recognition. However, while existing models demonstrate excellent classification accuracy, they generally suffer from insufficient interpretability. Shapelet-based methods offer interpretability advantages, yet existing models like ShapeFormer suffer from high computational resource consumption and low training efficiency during shapelet discovery and training phases, limiting their applicability in complex sensor time series classification tasks. To address this, our research proposes Efficiency ShapeFormer (EffiShapeFormer), an efficient time series classification framework, based on the latest shapelet model ShapeFormer. During the Shapelet Discovery phase, EffiShapeFormer introduces a dual-filtering mechanism. The Coarse Screening module efficiently identifies discriminative shapelets, while the Class-specific Representation module models these features to extract class-specific characteristics. Subsequently, in the Generic Representation stage, the proposed Convolution-Inverted Attention (CIA) module achieves synergistic integration of local feature extraction and global dependency modeling to capture cross-category generic features. Finally, the model fuses class-specific and generic features to achieve efficient and accurate time series classification. Experimental results on 22 sensor time series datasets demonstrate that EffiShapeFormer achieves higher average accuracy and F1-scores than baseline models, validating the proposed method's significant advantages in both efficiency and performance.