Nonlinear neural-like P model for time series classification

Abstract

Gated spiking neural P (GSNP) model is a novel recurrent model proposed recently, which is viewed as a variant of recurrent neural network. Time series classification (TSC) is one of the most challenging problems in data mining. In this work, based on the GSNP model, a nonlinear neural-like P (NNP) model for TSC is proposed. NNP model introduces the bidirectional mechanism and attention mechanism in deep learning, and builds nonlinear neuron modules together with GSNP to realize end-to-end learning. The neuron modules excavate the hidden features of time series to distinguish data and realize effective classification. Through evaluating on UCR Time Series Classification Archive, the classification performance of NNP model is significantly better than that of GSNP and other classification models. The NNP model can compete with advanced models on TSC task.