{"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
摘要
以时间序列数据的形式预测或预测股票价格仍然是经济论坛和金融市场持续讨论的热门话题。这篇文章分析了印度尼西亚在经历大流行和冠状病毒一年后的股价预测。本研究采用多种预测模型相结合的动态集成方法来提高预测精度。结果表明，该模型具有较高的精度，MAPE (Mean Absolute Percentage Error)值为0.003714125,RMSE (Root Mean Square Error)值为0.03958605。此外，这些结果可作为政府决策和投资者股票投资决策的依据。
Model Dynamic Ensemble Time Series untuk Prediksi Indeks Harga Saham Utama di Indonesia Pasca Pandemi
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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