Visual Analysis of Machine Learning Models for Multichannel Time Series Classification

Abstract

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 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.