Research Article Open Access

Forecasting the Number of Monthly Active Facebook and Twitter Worldwide Users Using ARMA Model

Qasem Abu Al-Haija1, Qian Mao1 and Kamal Al Nasr2
  • 1 Tennessee State University, United States
  • 2 University of Texas at San Antonio, United States
Journal of Computer Science
Volume 15 No. 4, 2019, 499-510

DOI: https://doi.org/10.3844/jcssp.2019.499.510

Submitted On: 30 January 2019 Published On: 19 April 2019

How to Cite: Abu Al-Haija, Q., Mao, Q. & Al Nasr, K. (2019). Forecasting the Number of Monthly Active Facebook and Twitter Worldwide Users Using ARMA Model. Journal of Computer Science, 15(4), 499-510. https://doi.org/10.3844/jcssp.2019.499.510

Abstract

In this study, an Auto-Regressive Moving Average (ARMA) Model with optimal order has been developed to estimate and forecast the short term future numbers of the monthly active Facebook and Twitter worldwide users. In order to pickup the optimal estimation order, we analyzed the model order vs. the corresponding model error in terms of final prediction error. The simulation results showed that the optimal model order to estimate the given Facebook and Twitter time series are ARMA[5, 5] and ARMA[3, 3], respectively, since they correspond to the minimum acceptable prediction error values. Besides, the optimal models recorded a high-level of estimation accuracy with fit percents of 98.8% and 96.5% for Facebook and Twitter time series, respectively. Eventually, the developed framework can be used accurately to estimate the spectrum for any linear time series.

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Keywords

  • Time Series
  • ARMA Model
  • Signal Estimation
  • Signal Prediction
  • Final Prediction Error
  • Facebook
  • Twitter