Time series prediction using artificial neural networks

Abstract

 In this work, artificial neural network (ANN) algorithms were used to predict time series of the oceanographic variables Southern Oscilation Index (SOI) and sea surface temperature anomaly (SSTA). The finite impulse response neural network (FIRNN) was applied to data obtained from the NOAA. In order to determine the most efficient FIRNN architecture, several experiments were made varying different parameters. The best predictions were obtained for a network with one input neuron and 10th-order filters in the input layer, two 8-neuron 5th-order filter hidden layers and one output neuron. All the networks were trained with the temporal backpropagation learning algorithm, using the sigmoid transfer function at the hidden layers and a linear output. The learning rate was 0.001. In most experiments a normalized mean square error of 0.4 ± 0.1 and a correlation coefficient between the original and the predicted series greater than 0.8 were found. From a comparison with other SSTA prediction methods, the results obtained with the neural network were the best ones for the short term forecasting case.