Most of the previous approaches to Time Series Classification (TSC) highlight the significance of receptive fields and frequencies while overlooking the time resolution. Hence, unavoidably suffered from scalability issues as they integrated an extensive range of receptive fields into classification models. Other methods, while having a better adaptation for large datasets, require manual design and yet not being able to reach the optimal architecture due to the uniqueness of each dataset. We overcome these challenges by proposing a novel multi-scale search space and a framework for Neural architecture search (NAS), which addresses both the problem of frequency and time resolution, discovering the suitable scale for a specific dataset. We further show that our model can serve as a backbone to employ a powerful Transformer module with both untrained and pre-trained weights. Our search space reaches the state-of-the-art performance on four datasets on four different domains while introducing more than ten highly fine-tuned models for each data.

翻译：以往大多数时间序列分类方法强调了感受野和频率的重要性，却忽略了时间分辨率。因此，这些方法在将大范围感受野集成到分类模型时不可避免地面临可扩展性问题。其他方法虽然对大规模数据集具有更好的适应性，但需要手动设计，且由于每个数据集的独特性，未能达到最优架构。为克服这些挑战，我们提出了一种新颖的多尺度搜索空间及神经架构搜索框架，该框架同时解决了频率与时间分辨率问题，能够为特定数据集发现合适的尺度。我们进一步证明，该模型可作为骨干架构，搭载强大的Transformer模块（支持未训练权重与预训练权重）。在四个不同领域的四个数据集上，我们的搜索空间达到了最先进的性能，同时为每个数据引入了十余种高度微调的模型。