在预测中应用人工神经网络模型

Rugia Elbashir, Khalid Genawi
{"title":"在预测中应用人工神经网络模型","authors":"Rugia Elbashir, Khalid Genawi","doi":"10.59992/ijsr.2024.v3n7p2","DOIUrl":null,"url":null,"abstract":"In this study we viewed concept of the Artificial Neural Networks (ANN) technology, and displayed its advantages, and its applications, and applied this technology in the prediction in time series, we were use the monthly closing price from Khartoum Stock Exchange index for the period from January 2012 to December 2021, to predict future values, by using software MATLAP R 2013a. The model building steps were done easily by MATLAB program, which selected the learning algorithm and functions to training the network automatically, we determined the number of layers and decay, the data series were divided to three sets: training, validation, and testing set. Depend on values of Mean Square Error (MSE) and Correlation Coefficient between target values and output values (R), the best forecasting model was selected, that has least (MSE) and high value of (R). The figure7 represent the predicted values were consistent with the real values of the series showing the efficiency of the model.","PeriodicalId":513336,"journal":{"name":"International Journal for Scientific Research","volume":"85 17","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Appling Artificial Neural Networks Models in Prediction\",\"authors\":\"Rugia Elbashir, Khalid Genawi\",\"doi\":\"10.59992/ijsr.2024.v3n7p2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study we viewed concept of the Artificial Neural Networks (ANN) technology, and displayed its advantages, and its applications, and applied this technology in the prediction in time series, we were use the monthly closing price from Khartoum Stock Exchange index for the period from January 2012 to December 2021, to predict future values, by using software MATLAP R 2013a. The model building steps were done easily by MATLAB program, which selected the learning algorithm and functions to training the network automatically, we determined the number of layers and decay, the data series were divided to three sets: training, validation, and testing set. Depend on values of Mean Square Error (MSE) and Correlation Coefficient between target values and output values (R), the best forecasting model was selected, that has least (MSE) and high value of (R). The figure7 represent the predicted values were consistent with the real values of the series showing the efficiency of the model.\",\"PeriodicalId\":513336,\"journal\":{\"name\":\"International Journal for Scientific Research\",\"volume\":\"85 17\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal for Scientific Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.59992/ijsr.2024.v3n7p2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal for Scientific Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.59992/ijsr.2024.v3n7p2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在本研究中,我们了解了人工神经网络(ANN)技术的概念,展示了其优势和应用,并将该技术应用于时间序列预测,我们使用 MATLAP R 2013a 软件,利用喀土穆证券交易所指数 2012 年 1 月至 2021 年 12 月期间的月收盘价预测未来值。建立模型的步骤由 MATLAB 程序轻松完成,它自动选择学习算法和函数来训练网络,我们确定了层数和衰减,数据序列被分为三组:训练集、验证集和测试集。根据目标值和输出值之间的均方误差(MSE)和相关系数(R)的值,选出了最小(MSE)和高(R)值的最佳预测模型。图 7 所示的预测值与序列的实际值一致,显示了模型的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Appling Artificial Neural Networks Models in Prediction
In this study we viewed concept of the Artificial Neural Networks (ANN) technology, and displayed its advantages, and its applications, and applied this technology in the prediction in time series, we were use the monthly closing price from Khartoum Stock Exchange index for the period from January 2012 to December 2021, to predict future values, by using software MATLAP R 2013a. The model building steps were done easily by MATLAB program, which selected the learning algorithm and functions to training the network automatically, we determined the number of layers and decay, the data series were divided to three sets: training, validation, and testing set. Depend on values of Mean Square Error (MSE) and Correlation Coefficient between target values and output values (R), the best forecasting model was selected, that has least (MSE) and high value of (R). The figure7 represent the predicted values were consistent with the real values of the series showing the efficiency of the model.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信