{"title":"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","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":"{\"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}","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}
Performance comparison and future estimation of time series data using predictive data mining techniques
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.