{"title":"Model Dynamic Ensemble Time Series untuk Prediksi Indeks Harga Saham Utama di Indonesia Pasca Pandemi","authors":"Evita Purnaningrum","doi":"10.36456/majeko.vol26.no1.a3949","DOIUrl":null,"url":null,"abstract":"Forecasting or predicting stock prices in the form of time series data is still a hot topic consistently discussed in economic forums and financial markets. This article had been analyzed prediction of stock prices in Indonesia after experiencing a pandemic and one year after the Corona virus. This study had been applied a dynamic ensemble method that combines various prediction models to improve forecasting accuracy. The results showed that the model has a high level of accuracy with MAPE (Mean Absolute Percentage Error) values of 0.003714125, and RMSE (Root Mean Square Error) of 0.03958605. Furthermore, these results could be used as a basis for government policy making and stock investment decisions for investors.","PeriodicalId":377832,"journal":{"name":"Majalah Ekonomi","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Majalah Ekonomi","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36456/majeko.vol26.no1.a3949","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Forecasting or predicting stock prices in the form of time series data is still a hot topic consistently discussed in economic forums and financial markets. This article had been analyzed prediction of stock prices in Indonesia after experiencing a pandemic and one year after the Corona virus. This study had been applied a dynamic ensemble method that combines various prediction models to improve forecasting accuracy. The results showed that the model has a high level of accuracy with MAPE (Mean Absolute Percentage Error) values of 0.003714125, and RMSE (Root Mean Square Error) of 0.03958605. Furthermore, these results could be used as a basis for government policy making and stock investment decisions for investors.
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