Open AccessJMASM 55: MATLAB Algorithms and Source Codes of 'cbnet' Function for Univariate Time Series Modeling with Neural Networks (MATLAB)
Author(s) -
Çağatay Bal,
Serdar Demir
Publication year - 2021
Publication title -
journal of modern applied statistical methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.169
H-Index - 28
ISSN - 1538-9472
DOI - 10.22237/jmasm/1608553080
Subject(s) - matlab , artificial neural network , univariate , computer science , series (stratigraphy) , algorithm , time series , nonparametric statistics , autoregressive model , function (biology) , machine learning , artificial intelligence , data mining , mathematics , statistics , multivariate statistics , paleontology , evolutionary biology , biology , operating system
Artificial Neural Networks (ANN) can be designed as a nonparametric tool for time series modeling. MATLAB serves as a powerful environment for ANN modeling. Although Neural Network Time Series Tool (ntstool) is useful for modeling time series, more detailed functions could be more useful in order to get more detailed and comprehensive analysis results. For these purposes, cbnet function with properties such as input lag generator, step-ahead forecaster, trial-error based network selection strategy, alternative network selection with various performance measure and global repetition feature to obtain more alternative network has been developed, and MATLAB algorithms and source codes has been introduced. A detailed comparison with the ntstool is carried out, showing that the cbnet function covers the shortcomings of ntstool.