Using Recurrent Neural Networks for Time Series Forecasting

In the past few years, artiicial neural networks (ANNs) have been investigated as a tool for time series analysis and forecasting. The most popular architecture is the multilayer perceptron, a feedforward network often trained by back-propagation. The forecasting performance of ANNs relative to traditional methods is still open to question although many experimenters seem optimistic. One problem with the multilayer perceptron is that, in its simplest form, it is similar to a pure autoregressive type model, so it lacks the ability to account for any moving average structure that may exist. By making a network recurrent, it is possible to include such structure. We present several examples showing how an ANN can be used to represent an ARMA scheme and compare the forecasting abilities of feedforward and recurrent neu-ral networks with traditional methods.