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.
glarma, ftsa, MARSS, ensembleBMA, ProbForecastGOP, forecast
TimeSeries, Bayesian, Econometrics, Environmetrics, Finance
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For attribution, please cite this work as
Dag & Yozgatligil, "The R Journal: GMDH: An R Package for Short Term Forecasting via GMDH-Type Neural Network Algorithms", The R Journal, 2016
BibTeX citation
@article{RJ-2016-028,
author = {Dag, Osman and Yozgatligil, Ceylan},
title = {The R Journal: GMDH: An R Package for Short Term Forecasting via GMDH-Type Neural Network Algorithms},
journal = {The R Journal},
year = {2016},
note = {https://doi.org/10.32614/RJ-2016-028},
doi = {10.32614/RJ-2016-028},
volume = {8},
issue = {1},
issn = {2073-4859},
pages = {379-386}
}