CLS Token Additional Embedding Method Using GASF and CNN for Transformer based Time Series Data Classification Tasks

Jaejin Seo, Sangwon Lee, Wonik Choi

http://doi.org/10.5626/JOK.2023.50.7.573

Time series data refer to a sequentially determined data set collected for a certain period of time. They are used for prediction, classification, and outlier detection. Although existing artificial intelligence models in the field of time series are mainly based on the Recurrent Neural Network, recent research trends are changing to transformer based models. Although these transformer based models show good performance for time series data prediction problem, they show relatively insufficient performance for classification tasks. In this paper, we propose an embedding method to add special classification tokens generated using Gramian Angular Summation Field and Convolution Neural Network to utilize time series data as input to transformers and found that we could leverage the pre-trained method to improve performance. To show the efficacy of our method, we conducted extensive experiments with 12 different models using the University of California, Riverside dataset. Experimental results show that our proposed model improved the average accuracy of 85 datasets from 1.4% to up to 21.1%.