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Forecasting of Primary Energy Consumption Data in the United State: a comparison between ARIMA and Holter Winters Models

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  • Rahman, Abdul
  • Ahmar, Ansari Saleh

    (Universitas Negeri Makassar)

Abstract

This research has a purpose to compare ARIMA Model and Holt-Winters Model based on MAE, RSS, MSE, and RMS criteria in predicting Primary Energy Consumption Total data in the US. The data from this research ranges from January 1973 to December 2016. This data will be processed by using R Software. Based on the results of data analysis that has been done, it is found that the model of Holt-Winters Additive type (MSE: 258350.1) is the most appropriate model in predicting Primary Energy Consumption Total data in the US. This model is more appropriate when compared with Holt-Winters Multiplicative type (MSE: 262260,4) and ARIMA Seasonal model (MSE: 723502,2). Paper ini dipresentasikan pada The 3th International Conference on Green Design and Manufacture 2017 (Krabi, Thailand, 29-30 April 2017), diunggah oleh Ansari Saleh Ahmar

Suggested Citation

  • Rahman, Abdul & Ahmar, Ansari Saleh, 2017. "Forecasting of Primary Energy Consumption Data in the United State: a comparison between ARIMA and Holter Winters Models," INA-Rxiv snxrq, Center for Open Science.
    • Handle: RePEc:osf:inarxi:snxrq
    DOI: 10.31219/osf.io/snxrq
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    References listed on IDEAS

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    1. Ansari Saleh Ahmar & Abdul Rahman & Andi Nurani Mangkawani Arifin & Alfatih Abqary Ahmar, 2017. "Predicting movement of stock of “Y” using Sutte Indicator," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1347123-134, January.
    2. Ansari Saleh Ahmar, 2017. "Sutte Indicator: A Technical Indicator in Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 7(2), pages 223-226.
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    Cited by:

    1. Ahmar, Ansari Saleh & Arifin, Andi Nurani Mangkawani, 2017. "Peramalan Indeks Harga Konsumen (IHK) Indonesia menggunakan forecast package pada R," INA-Rxiv bmwvy, Center for Open Science.
    2. Prado, Francisco & Minutolo, Marcel C. & Kristjanpoller, Werner, 2020. "Forecasting based on an ensemble Autoregressive Moving Average - Adaptive neuro - Fuzzy inference system – Neural network - Genetic Algorithm Framework," Energy, Elsevier, vol. 197(C).
    3. Agnieszka Mazurek-Czarnecka & Ksymena Rosiek & Marcin Salamaga & Krzysztof Wąsowicz & Renata Żaba-Nieroda, 2022. "Study on Support Mechanisms for Renewable Energy Sources in Poland," Energies, MDPI, vol. 15(12), pages 1-38, June.

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