Wulan Septya Zulmawati, Nonong amalita, Syafriandi Syafriandi, Admi Salma
{"title":"Bitcoin Price Prediction Using Support Vector Regression","authors":"Wulan Septya Zulmawati, Nonong amalita, Syafriandi Syafriandi, Admi Salma","doi":"10.24036/ujsds/vol1-iss5/121","DOIUrl":null,"url":null,"abstract":"Cryptocurrency provides the most return compared to other investment instruments, causing many novice traders to be attracted to crypto as a tool to make significant profits in the short term. One of the most widely used cryptocurrencies is Bitcoin. Trading is closely related to technical analysis. Various techniques in technical analysis cause beginner traders to have difficulties choosing the right technique. Machine learning methods can be an alternative to overcoming the barriers of beginner traders in the crypto market with predictive methods. One method of machine learning for prediction is Support Vector Regression (SVR). Using the Grid Search algorithm shows that this method has a good predictive accuracy value of 99,25% and MAPE 8,70%.","PeriodicalId":220933,"journal":{"name":"UNP Journal of Statistics and Data Science","volume":"207 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"UNP Journal of Statistics and Data Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24036/ujsds/vol1-iss5/121","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Cryptocurrency provides the most return compared to other investment instruments, causing many novice traders to be attracted to crypto as a tool to make significant profits in the short term. One of the most widely used cryptocurrencies is Bitcoin. Trading is closely related to technical analysis. Various techniques in technical analysis cause beginner traders to have difficulties choosing the right technique. Machine learning methods can be an alternative to overcoming the barriers of beginner traders in the crypto market with predictive methods. One method of machine learning for prediction is Support Vector Regression (SVR). Using the Grid Search algorithm shows that this method has a good predictive accuracy value of 99,25% and MAPE 8,70%.