Performance comparison and future estimation of time series data using predictive data mining techniques

Harshita Tanwar, Misha Kakkar
{"title":"Performance comparison and future estimation of time series data using predictive data mining techniques","authors":"Harshita Tanwar, Misha Kakkar","doi":"10.1109/ICDMAI.2017.8073477","DOIUrl":null,"url":null,"abstract":"Time series data mining techniques is applied to data related to women empowerment expenditure released from year 2006 to 2015 data for predicting future estimation of expenditure required for various schemes including all states of India. Two models namely linear regression model and ARIMA model are used for analyzing the future prediction of expenditure required for women empowerment in India. These two model are analyzed with respect to standardized error generated by them, fitted values, residuals, standardized error, square root standardized error are used for forecasting future expenditure prediction. Result shows that both model accurately and approximately predict the same future Expenditure measure.","PeriodicalId":368507,"journal":{"name":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Data Management, Analytics and Innovation (ICDMAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMAI.2017.8073477","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13

Abstract

Time series data mining techniques is applied to data related to women empowerment expenditure released from year 2006 to 2015 data for predicting future estimation of expenditure required for various schemes including all states of India. Two models namely linear regression model and ARIMA model are used for analyzing the future prediction of expenditure required for women empowerment in India. These two model are analyzed with respect to standardized error generated by them, fitted values, residuals, standardized error, square root standardized error are used for forecasting future expenditure prediction. Result shows that both model accurately and approximately predict the same future Expenditure measure.
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
使用预测数据挖掘技术的时间序列数据的性能比较和未来估计
时间序列数据挖掘技术应用于2006年至2015年公布的与妇女赋权支出有关的数据,用于预测包括印度所有邦在内的各种计划所需支出的未来估计。线性回归模型和ARIMA模型用于分析印度妇女赋权所需支出的未来预测。对这两个模型产生的标准化误差进行分析,拟合值、残差、标准化误差、平方根标准化误差用于预测未来支出预测。结果表明,这两种模型准确、近似地预测了相同的未来支出措施。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Keynote speakers The impact of corporate governance and firm performance on chief executive officer's compensation: Evidence from central state owned enterprises in India A novel method for evaluating intercept factor of solar line concentrator system Process trees & service chains can serve us to mitigate zero day attacks better Factors influencing herding behavior among Indian stock investors
×
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