Pattern recognition (psychology)

Feature (linguistics)

Biology

Discriminative model

Psychoanalysis

Philosophy

Series (stratigraphy)

Paleontology

Artificial intelligence

Psychology

Geography

Scale (ratio)

Visualization

Computer science

Linguistics

Feature extraction

Cartography

Confusion

Computer vision

Convolutional neural network

Pattern Recognition

Inspired by the great success of deep neural networks in image classification, recent works use Recurrence 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 confusion problem. They cannot represent the upward and downward trends of sequences effectively. In addition 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 represent 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 receptive 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 demonstrate the effectiveness of our method.

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