Abstract
The Facebook reactions were used over 300 billion times during their first year of existence. Research on reaction activity is essential especially for the digital marketing purpose. The market needs to understand how Facebook reactions fluctuate to forecast the best period to post advertisements on Facebook that yields the highest number of reactions. In this study, several time-series models are used to forecast the number of Facebook reactions over a certain period for different domains. A comparative study is done to evaluate the performance of each model, in terms of strengths and weaknesses.
Keywords: Forecasting, Facebook reactions, time series model, ARIMA, SARIMA
Authors
Yu Yong Poh [1] ; Lim Khai Yin [2] ; Lim Tong Ming [3]
[1][2] Faculty of Computing and Information Technology, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia
[1]tanwb@tarc.edu.my; [2]limtm@tarc.edu.my
Cite Me
Plain Text:
Y.P.Yu, K.Y.Lim, T.M.Lim, "A Comparative Study on the Time Series Models for Forecasting Facebook Reactions," International Conference on Digital Transformation and Applications (ICDXA) 2020, 2020, pp. 112-118, doi: https://doi.org/10.56453/icdxa.2020.1012.
BibTex:
@INPROCEEDINGS{ICDXA2020T205,
author={Yu, Yong Poh and Lim, Khai Yin and Lim, Tong Ming},
booktitle={International Conference on Digital Transformation and Applications (ICDXA) 2020},
title={A Comparative Study on the Time Series Models for Forecasting Facebook Reactions},
year={2020},
volume={},
number={},
pages={112-118},
doi={https://doi.org/10.56453/icdxa.2020.1012}}
