JMASM 55:用神经网络(MATLAB)进行单变量时间序列建模的“cbnet”函数的MATLAB算法和源代码

Cagatay Bal, S. Demir
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引用次数: 1

摘要

人工神经网络(ANN)可以被设计成一种用于时间序列建模的非参数工具。MATLAB为神经网络建模提供了强大的环境。尽管神经网络时间序列工具(ntstool)对时间序列建模很有用,但为了获得更详细、更全面的分析结果,更详细的函数可能更有用。为此,开发了cbnet函数,该函数具有输入滞后生成器、步进预报器、基于试错的网络选择策略、具有各种性能度量和全局重复特征的替代网络选择等特性,以获得更多的替代网络,并介绍了MATLAB算法和源代码。与ntstool进行了详细的比较,表明cbnet函数弥补了ntstool的不足。
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JMASM 55: MATLAB Algorithms and Source Codes of 'cbnet' Function for Univariate Time Series Modeling with Neural Networks (MATLAB)
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.
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来源期刊
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0.00%
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期刊介绍: The Journal of Modern Applied Statistical Methods is an independent, peer-reviewed, open access journal designed to provide an outlet for the scholarly works of applied nonparametric or parametric statisticians, data analysts, researchers, classical or modern psychometricians, and quantitative or qualitative methodologists/evaluators.
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