Autocorrelation and partial autocorrelation functions to improve neural networks models on univariate time series forecasting

Abstract

This paper proposes the autocorrelation function (acf) and partial autocorrelation function (pacf) as tools to help and improve the construction of the input layer for univariate time series artificial neural network (ANN) models, as used in classical time series analysis. Especially reducing the number of input layer neurons, and also helping the user to understand the behaviour of the series. Although the acf and pacf are considered linear functions, this paper shows that they can be used even in non linear time series. The ANNs used in this work are the Incremental Gaussian Mixture Network (IGMN), because it is a deterministic model, and the multilayer perceptron (MLP), the most used ANN model for time series forecasting.