Multi-Population O’Hare with ARIMA, ARIMA-GARCH and ANN in Forecasting Mortality Rate

IF 0.3 Q4 MATHEMATICS
Nurul Syuhada Samsudin, Siti Rohani Mohd Nor
{"title":"Multi-Population O’Hare with ARIMA, ARIMA-GARCH and ANN in Forecasting Mortality Rate","authors":"Nurul Syuhada Samsudin, Siti Rohani Mohd Nor","doi":"10.11113/matematika.v39.n3.1496","DOIUrl":null,"url":null,"abstract":"Multi-population mortality model has gained attention from prominent researchers of mortality due to its ability to provide biologically reasonable forecast. Previously, many researchers have proposed several multi-population stochastic mortality models that they considered adequate to produce accurate life expectancy. However, little have been addressed of the variability in full ages and time, which can contribute to an erroneous estimation of life expectancy. Therefore, this study proposed a new multi-population O’Hare with ARIMA, ARIMA-GARCH and ANN in forecasting the mortality rate for male and female in Malaysia, Taiwan, Japan, Hong Kong, Australia, USA, UK, Canada, and Switzerland. Multi-population O’Hare was used as a reference model, whilst ARIMA, ARIMA-GARCH and ANN were incorporated to the reference model to forecast the mortality rates. The adequacy of the proposed model was assessed by using measurement errors which were Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results showed by multi-population O’Hare with ARIMA-GARCH gave the best forecasting performance for Taiwan, Japan, Australia, USA, UK, Canada, and Switzerland. On the other hand, multi-population O’Hare with ARIMA gave the best forecasting performance for Malaysia, whereas multi-population O’Hare with ANN gave the best forecasting performance for Hong Kong.","PeriodicalId":43733,"journal":{"name":"Matematika","volume":null,"pages":null},"PeriodicalIF":0.3000,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Matematika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11113/matematika.v39.n3.1496","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICS","Score":null,"Total":0}
引用次数: 0

Abstract

Multi-population mortality model has gained attention from prominent researchers of mortality due to its ability to provide biologically reasonable forecast. Previously, many researchers have proposed several multi-population stochastic mortality models that they considered adequate to produce accurate life expectancy. However, little have been addressed of the variability in full ages and time, which can contribute to an erroneous estimation of life expectancy. Therefore, this study proposed a new multi-population O’Hare with ARIMA, ARIMA-GARCH and ANN in forecasting the mortality rate for male and female in Malaysia, Taiwan, Japan, Hong Kong, Australia, USA, UK, Canada, and Switzerland. Multi-population O’Hare was used as a reference model, whilst ARIMA, ARIMA-GARCH and ANN were incorporated to the reference model to forecast the mortality rates. The adequacy of the proposed model was assessed by using measurement errors which were Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results showed by multi-population O’Hare with ARIMA-GARCH gave the best forecasting performance for Taiwan, Japan, Australia, USA, UK, Canada, and Switzerland. On the other hand, multi-population O’Hare with ARIMA gave the best forecasting performance for Malaysia, whereas multi-population O’Hare with ANN gave the best forecasting performance for Hong Kong.
使用 ARIMA、ARIMA-GARCH 和 ANN 预测死亡率的多人口奥黑尔模型
多人口死亡率模型因其能够提供生物学上合理的预测而受到死亡率领域著名研究人员的关注。在此之前,许多研究人员提出了几种多人口随机死亡率模型,他们认为这些模型足以得出准确的预期寿命。然而,这些模型很少涉及全年龄和时间的变异性,而这种变异性可能导致对预期寿命的错误估计。因此,本研究提出了一种新的多人口 O'Hare,用 ARIMA、ARIMA-GARCH 和 ANN 预测马来西亚、台湾、日本、香港、澳大利亚、美国、英国、加拿大和瑞士的男性和女性死亡率。多人口 O'Hare 被用作参考模型,而 ARIMA、ARIMA-GARCH 和 ANN 被纳入参考模型以预测死亡率。利用测量误差(平均绝对百分比误差 (MAPE) 和均方根误差 (RMSE))评估了拟议模型的适当性。结果显示,在台湾、日本、澳大利亚、美国、英国、加拿大和瑞士,多人口 O'Hare 与 ARIMA-GARCH 的预测效果最佳。另一方面,使用 ARIMA 的多人口 O'Hare 对马来西亚的预测效果最好,而使用 ANN 的多人口 O'Hare 对香港的预测效果最好。
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
Matematika
Matematika MATHEMATICS-
自引率
25.00%
发文量
0
审稿时长
24 weeks
×
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