Auto-adaptive Multilayer Perceptron for Univariate Time Series Classification

Abstract

Time Series Classification (TSC) is an intricate problem that has encountered applications in various science fields. Accordingly, many researchers have presented interesting proposals to tackle the TSC problem. Nevertheless, most methods are hand-crafted to classify specific Time Series (TS) and are computationally expensive even for small data sets. In this paper, we propose a new approach to the Multilayer Perceptron (MLP) for TSC. The main novelty is that the hyperparameters related to batch size and the number of neurons in the hidden layers are auto-adapted according to the TS nature. We carried out an empirical study on 61 benchmark data sets from the University of California, Riverside (UCR). The experimental evaluation revealed that our proposal is competitive when we compare the accuracy versus 14 state-of-the-art methods. A non-parametric statistical test verifies that the proposed MLP ranked in fourth place and can be executed on standard computer equipment, making it simple, accessible, and competitive.