ECRTime: Ensemble Integration of Classification and Retrieval for Time Series Classification

TL;DR

ECRTime introduces an ensemble method combining classification and retrieval techniques for time series classification, addressing inter-class similarity issues and outperforming existing deep learning models in accuracy and efficiency.

Contribution

The paper presents ECR, a novel deep learning-based retrieval approach for TSC, and develops ECRTime, an ensemble that achieves state-of-the-art accuracy with reduced training time.

Findings

ECR outperforms existing deep learning methods on 112 UCR datasets.

ECRTime surpasses InceptionTime in accuracy.

ECRTime reduces training time while maintaining scalability.

Abstract

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.