对随机森林方法的游客数量的预测,在NTB省是单次平滑和双重精益

Ristu Haiban Hirzi, Umam Hidayaturrohman, Kertanah Kertanah, M. Amaly, Rody Satriawan
{"title":"对随机森林方法的游客数量的预测,在NTB省是单次平滑和双重精益","authors":"Ristu Haiban Hirzi, Umam Hidayaturrohman, Kertanah Kertanah, M. Amaly, Rody Satriawan","doi":"10.34312/jjps.v4i1.17088","DOIUrl":null,"url":null,"abstract":"The aim of study is to forecast global tourist visits and compare the forecasting methods to determine the best method using random forest, single exponential smoothing and double exponential smoothing, respectively. These methods are applied in global tourist visit data in West Nusa Tenggara Province. Random forest, single exponential smoothing and double exponential smoothing are familiar methods and are frequently utilized in forecasting. In addition, the three methods have great accuracy for time series data, such as data of global tourist visits. The data used in this study is data of global tourist visits from 2014 to 2021 in West Nusa Tenggara province. Applying the random forest, single exponential smoothing and double exponential smoothing methods in forecasting, the result shows that double exponential smoothing method is the best, based on the smallest value of Mean Absolute Percentage Error (MAPE) of 325.759. The forecasting result found out that tourist visits will increase from previous time, starting from August, 2021 to July, 2021 with an estimated 847 to 1045 lives","PeriodicalId":315674,"journal":{"name":"Jambura Journal of Probability and Statistics","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediksi Jumlah Wisatawan Menggunakan Metode Random Forest, Single Exponential Smoothing dan Double Exponential Smoothing di Provinsi NTB\",\"authors\":\"Ristu Haiban Hirzi, Umam Hidayaturrohman, Kertanah Kertanah, M. Amaly, Rody Satriawan\",\"doi\":\"10.34312/jjps.v4i1.17088\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The aim of study is to forecast global tourist visits and compare the forecasting methods to determine the best method using random forest, single exponential smoothing and double exponential smoothing, respectively. These methods are applied in global tourist visit data in West Nusa Tenggara Province. Random forest, single exponential smoothing and double exponential smoothing are familiar methods and are frequently utilized in forecasting. In addition, the three methods have great accuracy for time series data, such as data of global tourist visits. The data used in this study is data of global tourist visits from 2014 to 2021 in West Nusa Tenggara province. Applying the random forest, single exponential smoothing and double exponential smoothing methods in forecasting, the result shows that double exponential smoothing method is the best, based on the smallest value of Mean Absolute Percentage Error (MAPE) of 325.759. The forecasting result found out that tourist visits will increase from previous time, starting from August, 2021 to July, 2021 with an estimated 847 to 1045 lives\",\"PeriodicalId\":315674,\"journal\":{\"name\":\"Jambura Journal of Probability and Statistics\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Jambura Journal of Probability and Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.34312/jjps.v4i1.17088\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jambura Journal of Probability and Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34312/jjps.v4i1.17088","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

研究的目的是预测全球游客数量,并比较随机森林、单指数平滑和双指数平滑的预测方法,以确定最佳的预测方法。将这些方法应用于西努沙登加拉省的全球游客访问量数据。随机森林、单指数平滑和双指数平滑是常用的预测方法。此外,这三种方法对时间序列数据(如全球游客数量数据)具有较高的精度。本研究使用的数据是2014年至2021年西努沙登加拉省全球游客访问量的数据。应用随机森林、单指数平滑和双指数平滑方法进行预测,结果表明,双指数平滑方法以平均绝对百分比误差(MAPE)最小值为325.759为最佳。预测结果显示,从2021年8月开始到2021年7月,游客人数将比以往有所增加,预计将有847 ~ 1045人死亡
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediksi Jumlah Wisatawan Menggunakan Metode Random Forest, Single Exponential Smoothing dan Double Exponential Smoothing di Provinsi NTB
The aim of study is to forecast global tourist visits and compare the forecasting methods to determine the best method using random forest, single exponential smoothing and double exponential smoothing, respectively. These methods are applied in global tourist visit data in West Nusa Tenggara Province. Random forest, single exponential smoothing and double exponential smoothing are familiar methods and are frequently utilized in forecasting. In addition, the three methods have great accuracy for time series data, such as data of global tourist visits. The data used in this study is data of global tourist visits from 2014 to 2021 in West Nusa Tenggara province. Applying the random forest, single exponential smoothing and double exponential smoothing methods in forecasting, the result shows that double exponential smoothing method is the best, based on the smallest value of Mean Absolute Percentage Error (MAPE) of 325.759. The forecasting result found out that tourist visits will increase from previous time, starting from August, 2021 to July, 2021 with an estimated 847 to 1045 lives
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:481959085
Book学术官方微信