Time series classification using deep residual probability random forests

Time series data widely exists in industrial, medical and other application fields. Due to its strong temporal correlation and large feature space dimension, traditional time series classification methods generally have problems of insufficient accuracy and complex feature engineering. This paper proposes an end-to-end unified deep learning model based on residual networks and probability decision trees by fully considering the superiority of deep neural networks in dealing with complex time series data and the strong ability of a decision tree method to fit data. This model uses a residual network to extract advanced features from original time series. In order to better establish the mapping relationship between features and labels, probability decision trees are integrated into the classification layer of the residual network. Meanwhile, the integration strategy of random subspace is designed to alleviate the over-fitting phenomenon caused by the deep structure of the residual network. We also give the iterative optimization scheme to jointly optimize model’s split parameters and prediction parameters. Experiments and analysis on a large number of benchmark datasets and an industrial case show that the classification performance of the proposed model is better than that of traditional methods and other deep learning methods, and the generalization ability of the residual network can be improved effectively. © 2024 Northeast University. All rights reserved.