GMDH-type neural network algorithms for short term forecasting

Download
index.pdf
2015
Dağ, Osman
Show full item record
217
views
109
downloads
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 mainly focused on development of free software. For this purpose, we developed an R package GMDH. Moreover, we integrated different transfer functions, sigmoid, radial basis, polynomial, and tangent functions, into GMDH algorithm. We proposed an algorithm in which all transfer functions are used simultaneously or separately if desired. Also, we used regularized least square estimation for the estimation of weights to overcome multi-collinearity problem. The methods were illustrated on real life datasets having different properties to see the prediction and forecasting performance of the algorithm. We included ARIMA models and exponential smoothing methods for the comparison purpose. GMDH algorithms show the same or even better performance than the other methods.
Time-series analysis., Least squares., Transfer functions., Statistics
http://etd.lib.metu.edu.tr/upload/12618978/index.pdf
https://hdl.handle.net/11511/24830
Graduate School of Natural and Applied Sciences, Thesis

Suggestions

GMDH: An R Package for Short Term Forecasting via GMDH-Type Neural Network Algorithms
DAĞ, OSMAN; Yozgatlıgil, Ceylan (2016-08-01)
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 n...
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...
Diverse classifiers ensemble based on GMDH-type neural network algorithm for binary classification
DAĞ, OSMAN; KAŞIKCI, MERVE; KARABULUT, ERDEM; Alpar, Reha (Informa UK Limited, 2019-12-03)
Group Method of Data Handling (GMDH) - type neural network algorithm is the heuristic self-organizing algorithm to model the sophisticated systems. In this study, we propose a new algorithm assembling different classifiers based on GMDH algorithm for binary classification. A Monte Carlo simulation study is conducted to compare diverse classifier ensemble based on GMDH (dce-GMDH) algorithm to the other well-known classifiers and to give recommendations for applied researchers on the selection of appropriate ...
Representing temporal knowledge in connectionist expert systems
Alpaslan, Ferda Nur (1996-09-27)
This paper introduces a new temporal neural networks model which can be used in connectionist expert systems. Also, a Variation of backpropagation algorithm, called the temporal feedforward backpropagation algorithm is introduced as a method for training the neural network. The algorithm was tested using training examples extracted from a medical expert system. A series of experiments were carried out using the temporal model and the temporal backpropagation algorithm. The experiments indicated that the alg...
Consensus clustering of time series data
Yetere Kurşun, Ayça; Batmaz, İnci; İyigün, Cem; Department of Scientific Computing (2014)
In this study, we aim to develop a methodology that merges Dynamic Time Warping (DTW) and consensus clustering in a single algorithm. Mostly used time series distance measures require data to be of the same length and measure the distance between time series data mostly depends on the similarity of each coinciding data pair in time. DTW is a relatively new measure used to compare two time dependent sequences which may be out of phase or may not have the same lengths or frequencies. DTW aligns two time serie...
Citation Formats
O. Dağ, “GMDH-type neural network algorithms for short term forecasting,” M.S. - Master of Science, Middle East Technical University, 2015.
METU IIS - Integrated Information Service open@metu.edu.tr