Spiking Echo State Convolutional Neural Network for Robust Time Series Classification

Abstract

In this paper, a novel high-accuracy and robust computing framework for time series classification tasks is presented. The framework consists of a feature extraction module and a classification module, where the feature extraction is implemented by reservoir computing method of spiking neurons, and the classification result is obtained by the state-of-the-art analog convolutional neural networks (CNNs). The original time series input is first converted to multi-channel spike streams, then fed into the spiking reservoir layer to produce intermediate spike output, and subsequently, the spike output is transformed into a 2D mapping image, and deep CNN model is applied to classify the mapping image. The proposed model has the following three significant advantages: long-and-short term memory brought by the echo state of reservoir component, robustness to noise brought by the spiking encoding method, and high-accuracy performance brought by the deep CNN model. The experiments conducted on both synthetic time series data set and UCR time series data sets showed that our approach achieved highly competitive accuracy and robustness over other existing methods.