Multi-scale signed recurrence plot based time series classification using inception architectural networks

Inspired by the great success of deep neural networks in image classification, recent works use Recur-rence Plots (RP) to encode time series as images for classification. RP provide rich texture information and construct long-term time correlations, which are effective supplements to the networks. However, RP cannot handle the scale and length variability of sequences. Moreover, RP have serious tendency con -fusion problem. They cannot represent the upward and downward trends of sequences effectively. In ad-dition to the defects of RP, existing time series classification (TSC) networks cannot adapt to the various scales of discriminative regions of time series effectively. To tackle these problems, this paper proposes a method, named MSRP-IFCN. It is composed of two submodules, the Multi-scale Signed RP (MSRP) and the Inception Fully Convolutional Network (IFCN). MSRP are proposed to handle the defects of RP. They comprise three components, namely the multi-scale RP, the asymmetric RP and the signed RP. We first use the multi-scale RP to enrich the scales of images. Then, the asymmetric RP are constructed to repre-sent long sequences. Finally, the signed RP images are obtained by multiplying the designed sign masks to remove the tendency confusion. Besides, IFCN is proposed to enhance the existing TSC networks in multi-scale feature extraction. By introducing the modified Inception modules, IFCN obtains extensive re-ceptive fields and better extracts multi-scale features from the MSRP images. Experimental results on 85 UCR datasets indicate the superior performance of MSRP-IFCN. The visualization results further demon-strate the effectiveness of our method.(c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Automated summary

This paper proposes a method called MSRP-IFCN for encoding time series as images for classification. The method uses multi-scale signed RP and Inception fully convolutional network to handle the scale and length variability of sequences and enhance multi-scale feature extraction. Experimental results demonstrate the superior performance of this method.