Predicting Gross Domestic Product Using Weighted Exponential Moving Average on Phatsa Web Application

  • Authors

    • Marcel Bonar Kristanda
    • Seng Hansun
    2018-12-16
    https://doi.org/10.14419/ijet.v7i4.40.24420
  • forecasting, gross domestic product, time-series, web application, weighted exponential moving average,

  • Gross Domestic Product (GDP) has been known as multi criteria measurement of market value from goods and services across nation. Even there has been an improvement study on accuracy recently, this subject still opens a huge potential of future research and method development and implementation. At this study, we compared a new approach on Moving Average method called Weighted Exponentially Moving Average (WEMA) that created in 2014. We implement the method on Phatsa Web Application and evaluate the results by calculating its Mean Squared Error (MSE), Mean Absolute Scaled Error (MASE), and Mean Absolute Percentage Error (MAPE). Comparison study with the conventional moving average methods named Simple Moving Average (SMA), Weighted Moving Average (WMA) and Exponentially Moving Average then conducted to review the capability of WEMA in predicting Indonesia’s GDP data. It is proven that WEMA can provide more accurate results rather than SMA and WMA, but it still has the same error rate value if compared to EMA with more computation time. In addition to the result, Czech’s GDP data set is also tested and provides more information on how WEMA calculation with expanded data set, and more volatile value compared to Indonesia’s GDP in Czech’s GDP data set.

     

     


     

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  • How to Cite

    Bonar Kristanda, M., & Hansun, S. (2018). Predicting Gross Domestic Product Using Weighted Exponential Moving Average on Phatsa Web Application. International Journal of Engineering & Technology, 7(4.40), 137-140. https://doi.org/10.14419/ijet.v7i4.40.24420