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Predicting Closed Price Time Series Data Using ARIMA Model

Author

Listed:
  • S. AL Wadi
  • Mohammad Almasarweh
  • Ahmed Atallah Alsaraireh

Abstract

Closed price forecasting plays a main rule in finance and economics which has encouraged the researchers to introduce a fit model in forecasting accuracy. The autoregressive integrated moving average (ARIMA) model has developed and implemented in many applications. Therefore, in this article the researchers utilize ARIMA model in predicting the closed time series data which have been collected from Amman Stock Exchange (ASE) from Jan. 2010 to Jan. 2018. As a result this article shows that the ARIMA model has significant results for short-term prediction. Therefore, these results will be helpful for the investments.

Suggested Citation

  • S. AL Wadi & Mohammad Almasarweh & Ahmed Atallah Alsaraireh, 2018. "Predicting Closed Price Time Series Data Using ARIMA Model," Modern Applied Science, Canadian Center of Science and Education, vol. 12(11), pages 181-181, November.
    • Handle: RePEc:ibn:masjnl:v:12:y:2018:i:11:p:181
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    References listed on IDEAS

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    2. Wang, Ju-Jie & Wang, Jian-Zhou & Zhang, Zhe-George & Guo, Shu-Po, 2012. "Stock index forecasting based on a hybrid model," Omega, Elsevier, vol. 40(6), pages 758-766.
    3. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    4. Wei, Liang-Ying, 2013. "A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX," Economic Modelling, Elsevier, vol. 33(C), pages 893-899.
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    Cited by:

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    2. Manlika Ratchagit & Honglei Xu, 2022. "A Two-Delay Combination Model for Stock Price Prediction," Mathematics, MDPI, vol. 10(19), pages 1-21, September.

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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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