I. Pratama, I. W. Sumarjaya, NI Luh Putu Suciptawati
{"title":"平稳过渡时市场比特币的管理自回归方法（STAR）","authors":"I. Pratama, I. W. Sumarjaya, NI Luh Putu Suciptawati","doi":"10.24843/mtk.2022.v11.i02.p367","DOIUrl":null,"url":null,"abstract":"One of the spectacular advances in technology in the economic field is the cryptocurrency it created. The fluctuating price of Bitcoin, is widely used as a means of making profit. The time series forecasting method that can be used for the case of nonlinear time series data such as Bitcoin data is the smooth transition autoregressive (STAR) model. STAR is an extension of the autoregressive model for nonlinear time data. The purpose of this study is to obtain the results of forecasting Bitcoin price data for the next 2 two months using the STAR method. The data used in this study is Bitcoin daily price data from September 2017 to April 2021. To estimate the STAR model, several things that must be determined are the autoregressive model, transition variables, and transition functions. If the STAR model has been estimated, forecasting will be carried out for the next 2 months, which results in the forecast for the highest Bitcoin price falling on June 30, 2021 and the lowest Bitcoin price falling on May 1, 2021.","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"PERAMALAN HARGA BITCOIN DENGAN METODE SMOOTH TRANSITION AUTOREGRESSIVE (STAR)\",\"authors\":\"I. Pratama, I. W. Sumarjaya, NI Luh Putu Suciptawati\",\"doi\":\"10.24843/mtk.2022.v11.i02.p367\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the spectacular advances in technology in the economic field is the cryptocurrency it created. The fluctuating price of Bitcoin, is widely used as a means of making profit. The time series forecasting method that can be used for the case of nonlinear time series data such as Bitcoin data is the smooth transition autoregressive (STAR) model. STAR is an extension of the autoregressive model for nonlinear time data. The purpose of this study is to obtain the results of forecasting Bitcoin price data for the next 2 two months using the STAR method. The data used in this study is Bitcoin daily price data from September 2017 to April 2021. To estimate the STAR model, several things that must be determined are the autoregressive model, transition variables, and transition functions. If the STAR model has been estimated, forecasting will be carried out for the next 2 months, which results in the forecast for the highest Bitcoin price falling on June 30, 2021 and the lowest Bitcoin price falling on May 1, 2021.\",\"PeriodicalId\":11600,\"journal\":{\"name\":\"E-Jurnal Matematika\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"E-Jurnal Matematika\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.24843/mtk.2022.v11.i02.p367\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"E-Jurnal Matematika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24843/mtk.2022.v11.i02.p367","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PERAMALAN HARGA BITCOIN DENGAN METODE SMOOTH TRANSITION AUTOREGRESSIVE (STAR)
One of the spectacular advances in technology in the economic field is the cryptocurrency it created. The fluctuating price of Bitcoin, is widely used as a means of making profit. The time series forecasting method that can be used for the case of nonlinear time series data such as Bitcoin data is the smooth transition autoregressive (STAR) model. STAR is an extension of the autoregressive model for nonlinear time data. The purpose of this study is to obtain the results of forecasting Bitcoin price data for the next 2 two months using the STAR method. The data used in this study is Bitcoin daily price data from September 2017 to April 2021. To estimate the STAR model, several things that must be determined are the autoregressive model, transition variables, and transition functions. If the STAR model has been estimated, forecasting will be carried out for the next 2 months, which results in the forecast for the highest Bitcoin price falling on June 30, 2021 and the lowest Bitcoin price falling on May 1, 2021.
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