Time Series Forecasting with Neural Networks and Choquet Integral

Abstract

The objective of this paper is to compare time series forecasting by using three different backpropagation neural networks. A daily time series of Vale Company in the period March 1, 2000-June 10, 2006 is used as a reference against which results from other forecasts are compared. Three types of backpropagation neural networks are constructed with different input layers: the first among those makes use of the real time series data; the second uses the normalized real time series data; and the third uses the normalized real time series data and the Choquet integral in order to fuzzify the input layer. In all of the three backpropagation neural networks a hidden layer with tangent sigmoid transfer function and different numbers of neurons are used. In the output of the three neural networks a linear transfer function with one neuron for obtaining a linear equation after their training is used. The forecasting equations obtained for each neural networks are used with outsample data to forecast Vale's time series data and compare against real data. On the basis of the obtained results we conclude that the use of Choquet integral in this context is powerful enough so that its use must be recommended.