Predicting Closed Price Time Series Data Using ARIMA Model


  •  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.



This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1913-1844
  • ISSN(Online): 1913-1852
  • Started: 2007
  • Frequency: monthly

Journal Metrics

(The data was calculated based on Google Scholar Citations)

h-index (January 2018): 30

i10-index (January 2018): 163

h5-index (January 2018): 19

h5-median(January 2018): 25

Contact