Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveS

Abstract

Nowadays, with the rising number of sensor signals 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 everhigher 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. We show that LightWaveS achieves accuracy comparable to recent MTSC models and speedup ranging from 9x to 53x compared to ROCKET during inference on an edge device, on datasets with comparable accuracy.