ViT-Based Multi-Scale Classification Using Digital Signal Processing and Image Transformation

The existing classification of time-series data has difficulties that traditional methodologies struggle to address, such as complexity and dynamic variation. Difficulty with pattern recognition and long-term dependency modeling, high dimensionality and complex interactions between variables, and incompleteness of irregular intervals, missing values, and noise are the main causes for the degradation of model performance. Therefore, it is necessary to develop new classification methodologies to effectively process time-series data and make real-world applications. Accordingly, this study proposes ViT-based multi-scale classification using digital signal processing and image transformation. It comprises feature extraction through digital signal processing (DSP), image transformation, and vision transformer (ViT) based classification. In the DSP stage, a total of five features are extracted through sampling, quantization, and discrete fourier transform (DFT), which are sampling time, sampled signal, quantized signal, and magnitudes and phases extracted through DFT processing. Subsequently, the extracted multi-scale features are used to generate new images. Finally, based on the generated images, a ViT model is applied to make multi-class classification. This study confirms the superiority of the proposed approach by comparing traditional models with ViT and convolutional neural network (CNN) models. Particularly, by showing excellent classification performance even for the most challenging classes, it proves effective data processing in terms of data diversity. Ultimately, this study suggests a methodology for the analysis and classification of time-series data and shows that it has the potential to be applied to a wide range of data analysis problems.