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

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    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.

     

     


     

  • Keywords


    forecasting;gross domestic product;time-series;web application;weighted exponential moving average;

  • References


      [1] Bank Indonesia, “Economic and Financial Data for Indonesia,” Economic and Financial Data for Indonesia, 21-Sep-2018. [Online]. Available: https://www.bi.go.id/sdds/. [Accessed: 23-Sep-2018].

      [2] A. Barnett, H. Mumtaz, and K. Theodoridis, “Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters,” International Journal of Forecasting, vol. 30, no. 1, pp. 129–143, 2014.

      [3] H. C. Bjørnland, F. Ravazzolo, and L. A. Thorsrud, “Forecasting GDP with global components: This time is different,” International Journal of Forecasting, vol. 33, no. 1, pp. 153–173, 2017.

      [4] T. E. Clark and F. Ravazzolo, “Macroeconomic forecasting performance under alternative specifications of time‐varying volatility,” Journal of Applied Econometrics, vol. 30, no. 4, pp. 551–575, 2015.

      [5] S. Hansun, “A Novel Research of New Moving Average Method in Time-series Analysis,” International Journal of New Media Technology, vol. 1, no. 1, pp. 22–26, 2014.

      [6] A. Ranjan and P. Jetley, “Evaluation of signal smoothing algorithms for stability of a quadrotor MAV,” in Autonomous Robot Systems and Competitions (ICARSC), 2014 IEEE International Conference on, 2014, pp. 309–314.

      [7] S. Hansun, “WEMA versus B-WEMA Methods in Forex Forecasting,” in Proceedings of the 9th International Conference on Machine Learning and Computing, 2017, pp. 268–271.

      [8] S. Hansun, “FX forecasting using B-WEMA: Variant of Brown’s Double Exponential Smoothing,” in Informatics and Computing (ICIC), International Conference on, 2016, pp. 262–266.

      [9] M. B. Kristanda and S. Hansun, “Phatsa: A web-based application for forecasting using conventional moving average methods,” in New Media Studies (CONMEDIA), 2017 4th International Conference on, 2017, pp. 38–43.

      [10] S. Hansun and M. B. Kristanda, “Performance analysis of conventional moving average methods in forex forecasting,” in Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS), 2017 International Conference on, 2017, pp. 11–17.

      [11] Y. Zhuang, L. Chen, X. S. Wang, and J. Lian, “A weighted moving average-based approach for cleaning sensor data,” in Distributed Computing Systems, 2007. ICDCS’07. 27th International Conference on, 2007, pp. 38–38.

      [12] I. Noda, “Adaptation of stepsize parameter to minimize exponential moving average of square error by newton’s method,” in 9th International Conference on Autonomous Agents and Multiagent Systems, pp. M-2–1, 2010.

      [13] Z. Wang and A. C. Bovik, “Mean squared error: Love it or leave it? A new look at signal fidelity measures,” IEEE signal processing magazine, vol. 26, no. 1, pp. 98–117, 2009.

      [14] R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast accuracy,” International journal of forecasting, vol. 22, no. 4, pp. 679–688, 2006.

      [15] L. Ezell, Practical CodeIgniter 3. Leanpub, 2015.

      [16] The Czech Statistical Office (CZSO), “Gross domestic product - time-series | CZSO,” Gross domestic product - time-series | CZSO, 31-Aug-2018. [Online]. Available: https://www.czso.cz/csu/czso/hdp_ts. [Accessed: 30-Sep-2018].


 

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Article ID: 24420
 
DOI: 10.14419/ijet.v7i4.40.24420




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