Title: Taking ROCKET on an efficiency mission: A distributed solution for fast and accurate multivariate time series classification. Abstract: 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 solutions. 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 solution 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 distributed solution for accurate MTSC, which is fast both during training and inference. Specifically, utilizing a wavelet scattering transformation of the time series and distributed feature selection, we manage to create a solution which employs just 2,5% of the ROCKET features, while achieving accuracy comparable to recent deep learning solutions. LightWaveS also scales well with more nodes and large numbers of channels. In addition, it can give interpretability into the nature of an MTSC problem and allows for tuning based on expert opinion. We present three versions of our algorithm and their results on training time, accuracy, inference speedup and scalability. We show that we achieve speedup ranging from 8x to 30x compared to ROCKET during inference on an edge device, on datasets with comparable accuracy.