Forecasting the Number of Monthly Active Facebook and Twitter Worldwide Users Using ARMA Model
- 1 Tennessee State University, United States
- 2 University of Texas at San Antonio, United States
Copyright: © 2020 Qasem Abu Al-Haija, Qian Mao and Kamal Al Nasr. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.
- 1,059 Views
- 450 Downloads
- 5 Citations
- Time Series
- ARMA Model
- Signal Estimation
- Signal Prediction
- Final Prediction Error