Visualizing the Machine Learning Process in Multichannel Time Series Classification

This paper uses visualization techniques to analyze the learning process of six machine learning classifiers for multichannel time series classification (MTSC), including five deep learning models—1D CNN, CNN-LSTM, ResNet, InceptionTime, and Transformer—and one non-deep learning method, ROCKET. Sixteen datasets from the University of East Anglia (UEA) multivariate time series repository were employed to assess and compare classifier performance. To explore how data characteristics influence accuracy, we applied channel selection, feature selection, and similarity analysis between training and testing sets. Visualization techniques were used to examine the temporal and structural patterns of each dataset, offering insight into how feature relevance, channel informativeness, and group separability affect model performance. The experimental results show that ROCKET achieves the most consistent accuracy across datasets, although its performance decreases with a very large number of channels. Conversely, the Transformer model underperforms in datasets with limited training instances per class. Overall, the findings highlight the importance of visual exploration in understanding MTSC behavior and indicate that channel relevance and data separability have a greater impact on classification accuracy than feature-level patterns.