Deep learning models have been shown to be a powerful solution for Time Series Classification (TSC). State-of-the-art architectures, while producing promising results on the UCR and the UEA archives , present a high number of trainable parameters. This can lead to long training with high CO2 emission, power consumption and possible increase in the number of FLoating-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 number of parameters of the state-of-the-art InceptionTime model, 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. To evaluate the performance of the proposed architecture on multivariate time series data, we adapt LITE to handle multivariate time series, we call this version LITEMV. To bring theory into application, we also conducted experiments using LITEMV on multivariate time series representing human rehabilitation movements, showing that LITEMV not only is the most efficient model but also the best performing for this application on the Kimore dataset, a skeleton based human rehabilitation exercises dataset. Moreover, to address the interpretability of LITEMV, we present a study using Class Activation Maps to understand the classification decision taken by the model during evaluation.

翻译：深度学习模型已被证明是时间序列分类任务中一种强大的解决方案。当前最先进的架构虽然在UCR和UEA基准数据集上取得了优异的结果，但其包含大量可训练参数。这可能导致训练时间延长、二氧化碳排放量增加、能耗上升，并可能提升每秒浮点运算次数。本文提出了一种用于时间序列分类的新型架构——采用增强技术的轻量化Inception架构，该架构仅包含当前最优模型InceptionTime参数量的2.34%，同时保持其性能。该架构通过使用深度可分离卷积技术，仅包含9,814个可训练参数，并融合了三种增强技术：多路复用、定制滤波器和空洞卷积。在UCR数据集上训练的LITE架构，其训练速度比InceptionTime快2.78倍，二氧化碳排放量和能耗降低2.79倍。为评估所提架构在多变量时间序列数据上的性能，我们将其适配为可处理多变量时间序列的版本LITEMV。为将理论应用于实践，我们在代表人体康复运动的多变量时间序列上进行了实验，结果表明在基于骨骼的人体康复训练数据集Kimore上，LITEMV不仅是最高效的模型，也是该应用场景中性能最优的模型。此外，为增强LITEMV的可解释性，我们利用类别激活映射技术开展研究，以理解模型在评估过程中做出分类决策的依据。