Dynamic Ensemble Time Series for Prediction Major Indices in Asean

  • Evita Purnaningrum Universitas PGRI Adi Buana Surabaya, Indonesia
  • Rina Fariana Universitas PGRI Adi Buana Surabaya, Indonesia
Keywords: forecasting, ensemble, time series, pandemic, stock price


World financial markets have been affected and depressed during the COVID-19 pandemic; all-digital capital market transactions experienced a sharp decline. No exception is the dynamics of capital markets in ASEAN countries. The uncertainty of the impact of the ASEAN Pandemic encourages stock price forecasting to reduce investment risk. It is also a topic that is consistently enthusiastically discussed in economic forums. This article applied stock price changes in five major ASEAN countries one year after the Coronavirus. Dynamic ensemble method that combines various predictive models to improve the accuracy of forecasts. The results showed that the model has a high level of accuracy with a small error value, which is below 1.5% for MAPE (Mean Absolute Percentage Error), and an average RMSE (Root Mean Square Error) of 5%. This suggests that investors could reduce their long-term investment risk by stealing the pandemic by using this model. In addition, these results are committed to being used as a basis for policy and decision-making for investors


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How to Cite
Purnaningrum, E., & Fariana, R. (2022). Dynamic Ensemble Time Series for Prediction Major Indices in Asean. Indonesian Journal of Social Research (IJSR), 4(1), 54-62. https://doi.org/10.30997/ijsr.v4i1.171