  Deep learning-based methods for Time Series Classification (TSC) typically
utilize deep networks to extract features, which are then processed through a
combination of a Fully Connected (FC) layer and a SoftMax function. However, we
have observed the phenomenon of inter-class similarity and intra-class
inconsistency in the datasets from the UCR archive and further analyzed how
this phenomenon adversely affects the "FC+SoftMax" paradigm. To address the
issue, we introduce ECR, which, for the first time to our knowledge, applies
deep learning-based retrieval algorithm to the TSC problem and integrates
classification and retrieval models. Experimental results on 112 UCR datasets
demonstrate that ECR is state-of-the-art(sota) compared to existing deep
learning-based methods. Furthermore, we have developed a more precise
classifier, ECRTime, which is an ensemble of ECR. ECRTime surpasses the
currently most accurate deep learning classifier, InceptionTime, in terms of
accuracy, achieving this with reduced training time and comparable scalability.
