Mo-BoNet: A TIME SERIES CLASSIFICATION MODEL BASED ON COMPUTER VISION

Abstract

Time series are widely distributed in many fields. Classical statistical methods are difficult to model the deep meaning of time series, and the deep learning methods based on recurrent neural network has great limitations when it is applied to indefinite long time series. In order to solve the above problems, a time series classification model based on computer vision is proposed, which transforms the time series classification problem into image classification problem. Firstly, three kinds of images with different linewidth corresponding to the time series are used as input to reduce the information loss in the conversion process. Secondly, the transfer learning model based on MobileNetV3-Large is used to encode the image data, and XGBoost is used for classification. The experimental results show that the classification effect of this model is better than that of the classical image classification model, and its XGBoost is also better than other ensemble methods, which proves the feasibility of computer vision method in time series classification task.