Performance Analysis and Evaluation of Regression Models for Sales Forecasting

Ann Baby, Antony Kevin Newniz
{"title":"Performance Analysis and Evaluation of Regression Models for Sales Forecasting","authors":"Ann Baby, Antony Kevin Newniz","doi":"10.1109/InCACCT57535.2023.10141828","DOIUrl":null,"url":null,"abstract":"Machine learning is transforming every sector of today’s world. By forecasting or forecasting sales, one can maximize the profits of business-to-consumer (B2C) models involving retail chains. This study examines aspects of predicting marketing trades by means of data. The results of this study will benefit store managers to provide a broad analysis of various forecasting methods for retail chain sales forecasting. This research study is to compare forecasting models that can analyze the seasonality of sales. Comparisons between different regression models such as linear regression, k-nearest neighbor, decision tree regression, random forest regression and XGB regression are performed and analyzed.","PeriodicalId":405272,"journal":{"name":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/InCACCT57535.2023.10141828","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Machine learning is transforming every sector of today’s world. By forecasting or forecasting sales, one can maximize the profits of business-to-consumer (B2C) models involving retail chains. This study examines aspects of predicting marketing trades by means of data. The results of this study will benefit store managers to provide a broad analysis of various forecasting methods for retail chain sales forecasting. This research study is to compare forecasting models that can analyze the seasonality of sales. Comparisons between different regression models such as linear regression, k-nearest neighbor, decision tree regression, random forest regression and XGB regression are performed and analyzed.
销售预测回归模型的性能分析与评价
机器学习正在改变当今世界的每一个领域。通过预测或预测销售,可以最大化涉及零售链的企业对消费者(B2C)模型的利润。本研究探讨了预测营销交易的数据方面。本研究的结果将有利于门店管理者对零售连锁店销售预测的各种预测方法进行广泛的分析。本研究的目的是比较能够分析销售季节性的预测模型。对线性回归、k近邻回归、决策树回归、随机森林回归和XGB回归等不同回归模型进行了比较分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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