An algorithm to improve the performance of convolutional neural networks for TSC tasks

Abstract

AbstractThe convolutional neural network (CNN) is an important network model for processing time series classification tasks. However, there can be a lot of noise in the data itself. The clarity of spatial feature expression of time‐series samples and the freeness and balance of data samples both have a big effect on how well CNNs can handle time series classification (TSC) tasks. This paper proposes a multi‐pose marking learning algorithm (MPML), which optimizes the spatial representation of data samples through multiple graphical representations of time series data and then solves the problems of freeness and heterogeneity of data samples through labeling and preferential learning of poorly trained sample data. The algorithm improves the model's robustness to noise by improving the spatial expression sentiment of the time series and the labeling learning of the feature data, thereby improving the convolutional neural network's performance to perform the TSC task. We show how well the algorithm works by testing it on 46 datasets from the UCR Time Series Classification Archive and comparing the results.