LITE: Light Inception with boosTing tEchniques for Time Series Classification
Ali Ismail-Fawaz, Maxime Devanne, Stefano Berretti, Jonathan Weber, Germain Forestier
Abstract
Deep learning models have been shown to be a powerful solution for Time Series Classification (TSC). State-of-the-art architectures, while conducting promising results on the UCR archive, present a high number of trainable parameters. This can lead to long training with a high CO2, Power consumption and possible increase in the number of FLoat-point Operation Per Second (FLOPS). In this paper, we present a new architecture for TSC, the Light Inception with boosTing tEchnique (LITE) with only 2.34% of the state-of-the-art model InceptionTime’s number of parameters, while preserving performance. This architecture, with only 9, 814 trainable parameters due to the usage of DepthWise Separable Convolutions (DWSC), is boosted by three techniques: multiplexing, custom filters, and dilated convolution. The LITE architecture, trained on the UCR, is 2.78 times faster than InceptionTime and consumes 2.79 times less CO2 and Power.