Time series forecasting using recurrent neural networks and wavelet reconstructed signals

Abstract

In this paper a novel neural network architecture for medium-term time series forecasting is presented. The proposed model, inspired on the Hybrid Complex Neural Network (HCNN) model, takes advantage of information obtained by wavelet decomposition and of the oscillatory abilities of recurrent neural networks (RNN). The prediction accuracy of the proposed architecture is evaluated using 11 economic time series of the NN5 Forecasting Competition for Artificial Neural Networks and Computational Intelligence, obtaining an average SMAPE of 27%. The proposed model shows a better mean performance in time series prediction of 56 values than a feed-forward network and a fully recurrent neural network with a similar number of nodes.