Forecasting the number of monthly active Facebook and Twitter worldwide users using ARMA model

Qasem Abu Al-Haija, Qian Mao, Kamal Al Nasr

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)499-510
Number of pages12
JournalJournal of Computer Science
Volume15
Issue number4
DOIs
StatePublished - 2019
Externally publishedYes

Keywords

  • ARMA model
  • Facebook
  • Final prediction error
  • Signal estimation
  • Signal prediction
  • Time series
  • Twitter

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Artificial Intelligence

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