RNTS: Robust Neural Temporal Search for Time Series Classification

DOI number:10.1109/IJCNN52387.2021.9534392

Journal:2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)

Place of Publication:ELECTR NETWORK

Key Words:Data mining,time series classification,deep learning,neural architecture search,convolutional neural network,LSTM

Abstract:Over the years, a large number of deep learning algorithms have been developed for time series classification (TSC). These algorithms were usually invented by researchers with prior knowledge and experience. However, it is a critical challenge for beginners to design decent structures to address various TSC problems. To this end, we propose a robust neural temporal search (RNTS) framework for identifying the relationships and features in TSC data, which mainly contains a temporal search network and an attentional LSTM network. To be specific, inspired by the idea of neural architecture search (NAS), the temporal search network automatically transforms its structure for each dataset according to its characteristics, responsible for extracting basic features. The attentional LSTM network is used to explore the complex shapelets and relationships the former may ignore. Experimental results demonstrate that RNTS achieves the best overall performance on 24 standard datasets selected from the UCR 2018 archive, in terms of three measures based on the top-1 accuracy, compared with a number of state-of-the-art approaches.

Co-author:Zhiwen Xiao,Xin Xu,Huanlai Xing*,Rong Qu,Fuhong Song,Bowen Zhao

Document Code:10.1109/IJCNN52387.2021.9534392

ISSN No.:2161-4393

Translation or Not:no

Date of Publication:2022-01-07