  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.
