安得拉邦水稻生产的ARIMA和NNAR模型的比较

G Vijayalakshmi, K Pushpanjali, Dr. A Mohan Babu
{"title":"安得拉邦水稻生产的ARIMA和NNAR模型的比较","authors":"G Vijayalakshmi, K Pushpanjali, Dr. A Mohan Babu","doi":"10.22271/maths.2023.v8.i3c.1041","DOIUrl":null,"url":null,"abstract":"If the data is linear and non-stationary, the models viz. Auto-Regressive (AR), Moving Average (MA), and Auto-Regressive Moving Average (ARMA) models cannot be used. So, an important forecasting technique called Auto-Regressive Integrated Moving Average (ARIMA) with (p, d, q) terms can be used. The best feature of Artificial Neural Networks when it is applied to forecasting data is its inherent capability of nonlinear modeling without any presumption about the statistical distribution of the given data. Model selection criteria based on RMSE for ARIMA and Neural Network Autoregressive (NNAR) models are computed. An appropriate model has to be framed effectively for the production wheat data in the state of Andhra Pradesh taken during the period from 1982 to 2022 (for 40 years).","PeriodicalId":500025,"journal":{"name":"International journal of statistics and applied mathematics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A comparison of ARIMA and NNAR models for production of rice in the state of Andhra Pradesh\",\"authors\":\"G Vijayalakshmi, K Pushpanjali, Dr. A Mohan Babu\",\"doi\":\"10.22271/maths.2023.v8.i3c.1041\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"If the data is linear and non-stationary, the models viz. Auto-Regressive (AR), Moving Average (MA), and Auto-Regressive Moving Average (ARMA) models cannot be used. So, an important forecasting technique called Auto-Regressive Integrated Moving Average (ARIMA) with (p, d, q) terms can be used. The best feature of Artificial Neural Networks when it is applied to forecasting data is its inherent capability of nonlinear modeling without any presumption about the statistical distribution of the given data. Model selection criteria based on RMSE for ARIMA and Neural Network Autoregressive (NNAR) models are computed. An appropriate model has to be framed effectively for the production wheat data in the state of Andhra Pradesh taken during the period from 1982 to 2022 (for 40 years).\",\"PeriodicalId\":500025,\"journal\":{\"name\":\"International journal of statistics and applied mathematics\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of statistics and applied mathematics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22271/maths.2023.v8.i3c.1041\",\"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 of statistics and applied mathematics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22271/maths.2023.v8.i3c.1041","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

如果数据是线性和非平稳的,则不能使用自回归(AR)、移动平均(MA)和自回归移动平均(ARMA)模型。因此,可以使用一种重要的预测技术,称为具有(p, d, q)项的自回归综合移动平均(ARIMA)。人工神经网络应用于预测数据时的最大特点是其固有的非线性建模能力,无需对给定数据的统计分布进行任何假设。计算了基于RMSE的ARIMA和神经网络自回归(NNAR)模型的模型选择准则。必须为1982年至2022年(40年)期间安得拉邦的小麦生产数据有效地构建一个适当的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A comparison of ARIMA and NNAR models for production of rice in the state of Andhra Pradesh
If the data is linear and non-stationary, the models viz. Auto-Regressive (AR), Moving Average (MA), and Auto-Regressive Moving Average (ARMA) models cannot be used. So, an important forecasting technique called Auto-Regressive Integrated Moving Average (ARIMA) with (p, d, q) terms can be used. The best feature of Artificial Neural Networks when it is applied to forecasting data is its inherent capability of nonlinear modeling without any presumption about the statistical distribution of the given data. Model selection criteria based on RMSE for ARIMA and Neural Network Autoregressive (NNAR) models are computed. An appropriate model has to be framed effectively for the production wheat data in the state of Andhra Pradesh taken during the period from 1982 to 2022 (for 40 years).
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:604180095
Book学术官方微信