Artificial neural networks are suitable for many tasks in pattern recognition and machine learning. In this paper we present an APL system for forecasting univariate time series with artificial neural networks. Unlike conventional techniques for time series analysis, an artificial neural network needs little information about the time series data and can be applied to a broad range of problems. However, the problem of network ``tuning'' remains: parameters of the backpropagation algorithm as well as the network topology need to be adjusted for optimal performance. For our application, we conducted experiments to find the right parameters for a forecasting network. The artificial neural networks that were found delivered a better forecasting performance than results obtained by the well known ARIMA technique.
|© 1997 Gottfried Rudorfer, © 1994 ACM APL Quote Quad, 1515 Broadway, New York, N.Y. 10036, Abteilung für Angewandte Informatik, Wirtschaftsuniversität Wien, 3/23/1998|