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

Date

2016-08-01

Author

DAĞ, OSMAN
Yozgatlıgil, Ceylan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

190
views

0
downloads

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.

URI

https://hdl.handle.net/11511/55563

Journal

R JOURNAL

Collections

Department of Statistics, Article

Suggestions

OpenMETU
Core

GMDH-type neural network algorithms for short term forecasting Dağ, Osman; Yozgatlıgil, Ceylan; Department of Statistics (2015) Group Method of Data Handling (GMDH) - type neural network algorithms are the heuristic self-organization method for modelling the complex systems. GMDH algorithms are utilized for the variety of purposes, which are identification of physical laws, extrapolation of physical fields, pattern recognition, clustering, approximation of multidimensional processes, forecasting without models and so on. In this study, GMDH - type neural network algorithms were applied to make forecasts for time series data sets. We...
GMDH2: Binary Classification via GMDH-Type Neural Network Algorithms-R Package and Web-Based Tool DAĞ, OSMAN; KARABULUT, ERDEM; Alpar, Reha (Atlantis Press, 2019-01-01) Group method of data handling (GMDH)-type neural network algorithms are the self-organizing algorithms for modeling complex systems. GMDH algorithms are used for different objectives; examples include regression, classification, clustering, forecasting, and so on. In this paper, we present GMDH2 package to perform binary classification via GMDH-type neural network algorithms. The package offers two main algorithms: GMDH algorithm and diverse classifiers ensemble based on GMDH (dce-GMDH) algorithm. GMDH algo...
Multiobjective evolutionary feature subset selection algorithm for binary classification Deniz Kızılöz, Firdevsi Ayça; Coşar, Ahmet; Dökeroğlu, Tansel; Department of Computer Engineering (2016) This thesis investigates the performance of multiobjective feature subset selection (FSS) algorithms combined with the state-of-the-art machine learning techniques for binary classification problem. Recent studies try to improve the accuracy of classification by including all of the features in the dataset, neglecting to determine the best performing subset of features. However, for some problems, the number of features may reach thousands, which will cause too much computation power to be consumed during t...
Implicit monolithic parallel solution algorithm for seismic analysis of dam-reservoir systems Özmen, Semih; Kurç, Özgür; Department of Civil Engineering (2016) This research mainly focuses on developing a computationally scalable and efficient solution algorithm that can handle linear dynamic analysis of dam-reservoir interaction problem. Lagrangian fluid finite elements are utilized and compressibility and viscosity of the fluid are taken into consideration during the reservoir modeling. In order to provide computational scalability and efficiency, domain decomposition methods implemented with parallel computing approaches such as Finite Element Tearing and Inter...
Electromagnetic Target Classification using time frequency analysis and neural networks Sayan, Gönül; Leblebicioğlu, Mehmet Kemal (Wiley, 1999-04-01) This paper demonstrates the feasibility and advantages of using a self-organizing map (SOM)-type neural network classifier for electromagnetic target recognition. The classifier is supported by a novel feature extraction unit in which the Wigner distribution (WD), a time-frequency representation, is utilized for the extraction of natural-resonance-related energy feature vectors from scattered fields. The proposed target classification technique is tested for a set of canonical targets, displaying an excelle...

Citation Formats

O. DAĞ and C. Yozgatlıgil, “GMDH: An R Package for Short Term Forecasting via GMDH-Type Neural Network Algorithms,” R JOURNAL, pp. 379–386, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55563.