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 language | English (US) |
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Pages (from-to) | 499-510 |
Number of pages | 12 |
Journal | Journal of Computer Science |
Volume | 15 |
Issue number | 4 |
DOIs | |
State | Published - 2019 |
Externally published | Yes |
Keywords
- ARMA model
- Final prediction error
- Signal estimation
- Signal prediction
- Time series
ASJC Scopus subject areas
- Software
- Computer Networks and Communications
- Artificial Intelligence