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.

通过提出一个新的多尺度搜索空间和神经架构搜索（NAS）框架，解决了时间序列分类中接收域、频率和时间分辨率的问题，并展示了该模型在四个不同领域的四个数据集上达到了最先进的性能，同时为每个数据集引入了十多个高度微调的模型。

MSTAR: 用于时间序列分类的多尺度骨干架构搜索