  Most current multivariate time series (MTS) classification algorithms focus
on improving the predictive accuracy. However, for large-scale (either
high-dimensional or long-sequential) time series (TS) datasets, there is an
additional consideration: to design an efficient network architecture to reduce
computational costs such as training time and memory footprint. In this work we
propose a methodology based on module-wise pruning and Pareto analysis to
investigate the relationship between model efficiency and accuracy, as well as
its complexity. Comprehensive experiments on benchmark MTS datasets illustrate
the effectiveness of our method.
