  Nowadays, with the rising number of sensors in sectors such as healthcare and
industry, the problem of multivariate time series classification (MTSC) is
getting increasingly relevant and is a prime target for machine and deep
learning approaches. Their expanding adoption in real-world environments is
causing a shift in focus from the pursuit of ever-higher prediction accuracy
with complex models towards practical, deployable solutions that balance
accuracy and parameters such as prediction speed. An MTSC model that has
attracted attention recently is ROCKET, based on random convolutional kernels,
both because of its very fast training process and its state-of-the-art
accuracy. However, the large number of features it utilizes may be detrimental
to inference time. Examining its theoretical background and limitations enables
us to address potential drawbacks and present LightWaveS: a framework for
accurate MTSC, which is fast both during training and inference. Specifically,
utilizing wavelet scattering transformation and distributed feature selection,
we manage to create a solution that employs just 2.5% of the ROCKET features,
while achieving accuracy comparable to recent MTSC models. LightWaveS also
scales well across multiple compute nodes and with the number of input channels
during training. In addition, it can significantly reduce the input size and
provide insight to an MTSC problem by keeping only the most useful channels. We
present three versions of our algorithm and their results on distributed
training time and scalability, accuracy, and inference speedup. We show that we
achieve speedup ranging from 9x to 53x compared to ROCKET during inference on
an edge device, on datasets with comparable accuracy.
