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

• URL: http://arxiv.org/abs/2204.01379v2
• Date: Tue, 5 Apr 2022 08:27:56 GMT
• Title: Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveS
• Authors: Leonardos Pantiskas, Kees Verstoep, Mark Hoogendoorn, Henri Bal
• Abstract summary: We present LightWaveS, a framework for accurate multivariate time series classification. It employs just 2.5% of the ROCKET features, while achieving accuracy comparable to recent deep learning models. We show that we achieve speedup ranging from 9x to 65x compared to ROCKET during inference on an edge device.
• Score: 3.5786621294068373
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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 which employs just 2.5% of the ROCKET features, while achieving accuracy comparable to recent deep learning 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 65x compared to ROCKET during inference on an edge device, on datasets with comparable accuracy.

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