GMDH: An R Package for Short Term Forecasting via GMDH-Type Neural Network Algorithms

Authors

Osman Dag, Hacettepe University
Ceylan Yozgatligil, Middle East Technical University

Date of this Version

8-2016

Document Type

Article

Citation

The R Journal (August 2016) 8(1); Editor: Michael Lawrence

Comments

Copyright 2016, The R Foundation. Open access material. License: CC BY 3.0 Unported

Abstract

Group Method of Data Handling (GMDH)-type neural network algorithms are the heuristic self organization method for the modelling of complex systems. GMDH algorithms are utilized for a variety of purposes, examples include identification of physical laws, the extrapolation of physical fields, pattern recognition, clustering, the approximation of multidimensional processes, forecasting without models, etc. In this study, the R package GMDH is presented to make short term forecasting through GMDH-type neural network algorithms. The GMDH package has options to use different transfer functions (sigmoid, radial basis, polynomial, and tangent functions) simultaneously or separately. Data on cancer death rate of Pennsylvania from 1930 to 2000 are used to illustrate the features of the GMDH package. The results based on ARIMA models and exponential smoothing methods are included for comparison.