股票价格指数(IHSG)使用Thresholding方法进行分析

Zahra Awaliya Fauziah, Junaidi, Lilies Handayani
{"title":"股票价格指数(IHSG)使用Thresholding方法进行分析","authors":"Zahra Awaliya Fauziah, Junaidi, Lilies Handayani","doi":"10.22487/25411969.2019.v8.i3.14962","DOIUrl":null,"url":null,"abstract":"Stock is one type of long-term investment in the capital market. The stock movement indicator that is most often used in analysis by investors is the  Indonesia Composite Index (ICI). ICI data is a variety of time series data, so it can be analyzed using forecasting. One forecasting method that can be used is the wavelet thresholding method. The wavelet threshold can analyze stationary, non-stationary, and nonlinear time series data by producing smooth estimates. The wavelet threshold has a wavelet filter and threshold parameters and threshold functions that can be used in analyzing. In this study MSE was assessed from several wavelet filters namely haar, daubechies, and coiflets filters at levels 1 to 7 with the thresholding function namely soft thresholding and thresholding parameters, namely minimax thresholding and sure thresholding. The data used is IHGS data in 2018 totaling 240 data. Based on the data analysis performed, MSE was obtained which means that the best filter provided in order 2 wavelet coiflet filter at level 2 and thresholding parameter is sure of thresholding with MSE value of 0.0094","PeriodicalId":399499,"journal":{"name":"Natural Science: Journal of Science and Technology","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analisis Indeks Harga Saham Gabungan (IHSG) Menggunakan Metode Wavelet Thresholding\",\"authors\":\"Zahra Awaliya Fauziah, Junaidi, Lilies Handayani\",\"doi\":\"10.22487/25411969.2019.v8.i3.14962\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Stock is one type of long-term investment in the capital market. The stock movement indicator that is most often used in analysis by investors is the  Indonesia Composite Index (ICI). ICI data is a variety of time series data, so it can be analyzed using forecasting. One forecasting method that can be used is the wavelet thresholding method. The wavelet threshold can analyze stationary, non-stationary, and nonlinear time series data by producing smooth estimates. The wavelet threshold has a wavelet filter and threshold parameters and threshold functions that can be used in analyzing. In this study MSE was assessed from several wavelet filters namely haar, daubechies, and coiflets filters at levels 1 to 7 with the thresholding function namely soft thresholding and thresholding parameters, namely minimax thresholding and sure thresholding. The data used is IHGS data in 2018 totaling 240 data. Based on the data analysis performed, MSE was obtained which means that the best filter provided in order 2 wavelet coiflet filter at level 2 and thresholding parameter is sure of thresholding with MSE value of 0.0094\",\"PeriodicalId\":399499,\"journal\":{\"name\":\"Natural Science: Journal of Science and Technology\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Natural Science: Journal of Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22487/25411969.2019.v8.i3.14962\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Natural Science: Journal of Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22487/25411969.2019.v8.i3.14962","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

股票是资本市场上的一种长期投资。投资者在分析中最常用的股票走势指标是印度尼西亚综合指数(ICI)。ICI数据是多种时间序列数据,因此可以用预测方法进行分析。一种可用的预测方法是小波阈值法。小波阈值可以通过产生平滑估计来分析平稳、非平稳和非线性时间序列数据。小波阈值具有小波滤波器和阈值参数以及可用于分析的阈值函数。在本研究中,使用阈值函数即软阈值和阈值参数即极大极小阈值和确定阈值,从1至7级的haar、daubechies和coiflets几个小波滤波器评估MSE。使用的数据为2018年的IHGS数据,总计240条数据。根据所做的数据分析,得到了MSE,即在2级小波锥波滤波器中提供的最佳滤波器,阈值参数确定阈值,MSE值为0.0094
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analisis Indeks Harga Saham Gabungan (IHSG) Menggunakan Metode Wavelet Thresholding
Stock is one type of long-term investment in the capital market. The stock movement indicator that is most often used in analysis by investors is the  Indonesia Composite Index (ICI). ICI data is a variety of time series data, so it can be analyzed using forecasting. One forecasting method that can be used is the wavelet thresholding method. The wavelet threshold can analyze stationary, non-stationary, and nonlinear time series data by producing smooth estimates. The wavelet threshold has a wavelet filter and threshold parameters and threshold functions that can be used in analyzing. In this study MSE was assessed from several wavelet filters namely haar, daubechies, and coiflets filters at levels 1 to 7 with the thresholding function namely soft thresholding and thresholding parameters, namely minimax thresholding and sure thresholding. The data used is IHGS data in 2018 totaling 240 data. Based on the data analysis performed, MSE was obtained which means that the best filter provided in order 2 wavelet coiflet filter at level 2 and thresholding parameter is sure of thresholding with MSE value of 0.0094
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