利用模糊ARTMAP神经网络预测能量时间序列数据

Willian de Assis Pedrobon Ferreira, I. Grout, Alexandre César Rodrigues da Silva
{"title":"利用模糊ARTMAP神经网络预测能量时间序列数据","authors":"Willian de Assis Pedrobon Ferreira, I. Grout, Alexandre César Rodrigues da Silva","doi":"10.1109/ICPEI49860.2020.9431435","DOIUrl":null,"url":null,"abstract":"Time-series forecasting is an important field of machine learning and is fundamental in analyzing trends based on historical data from various sources. In this paper, a fuzzy ARTMAP neural network for time-series forecasting is presented. To validate the proposed system, two energy-related datasets from Great Britain were selected. With a promising processing time and accuracy as good as a traditional machine learning algorithm, the fuzzy ARTMAP neural network has shown that can be a good option to perform forecasting considering different time-based data issues.","PeriodicalId":342582,"journal":{"name":"2020 International Conference on Power, Energy and Innovations (ICPEI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Forecasting energy time-series data using a fuzzy ARTMAP neural network\",\"authors\":\"Willian de Assis Pedrobon Ferreira, I. Grout, Alexandre César Rodrigues da Silva\",\"doi\":\"10.1109/ICPEI49860.2020.9431435\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Time-series forecasting is an important field of machine learning and is fundamental in analyzing trends based on historical data from various sources. In this paper, a fuzzy ARTMAP neural network for time-series forecasting is presented. To validate the proposed system, two energy-related datasets from Great Britain were selected. With a promising processing time and accuracy as good as a traditional machine learning algorithm, the fuzzy ARTMAP neural network has shown that can be a good option to perform forecasting considering different time-based data issues.\",\"PeriodicalId\":342582,\"journal\":{\"name\":\"2020 International Conference on Power, Energy and Innovations (ICPEI)\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Power, Energy and Innovations (ICPEI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPEI49860.2020.9431435\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Power, Energy and Innovations (ICPEI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPEI49860.2020.9431435","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

时间序列预测是机器学习的一个重要领域,是基于各种来源的历史数据分析趋势的基础。本文提出了一种用于时间序列预测的模糊ARTMAP神经网络。为了验证所提出的系统,选择了来自英国的两个能源相关数据集。模糊ARTMAP神经网络具有与传统机器学习算法一样好的处理时间和精度,可以作为考虑不同基于时间的数据问题的预测的好选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Forecasting energy time-series data using a fuzzy ARTMAP neural network
Time-series forecasting is an important field of machine learning and is fundamental in analyzing trends based on historical data from various sources. In this paper, a fuzzy ARTMAP neural network for time-series forecasting is presented. To validate the proposed system, two energy-related datasets from Great Britain were selected. With a promising processing time and accuracy as good as a traditional machine learning algorithm, the fuzzy ARTMAP neural network has shown that can be a good option to perform forecasting considering different time-based data issues.
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
期刊最新文献
Analysis and Design of Wireless Charging Lane for Light Rail Transit Tie-Line Constrained Multi-Area Generation Scheduling Using Mixed Integer Programming Part I: Problem Formulation Intelligent Machine Learning Techniques for Condition Assessment of Power Transformers Energy Consumption Study of Rapid Charging of Catenary Free Light Rail Transit A Dual Band Split Ring Electromagnetic Band Gap using Interdigital Technique and its Applications
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1