Browse > Article
http://dx.doi.org/10.21219/jitam.2019.26.2.061

Forecasting Demand of 5G Internet of things based on Bayesian Regression Model  

Park, Kyung Jin (ETRI & UST)
Kim, Taehan (ETRI & UST)
Publication Information
Journal of Information Technology Applications and Management / v.26, no.2, 2019 , pp. 61-73 More about this Journal
Abstract
In 2019, 5G mobile communication technology will be commercialized. From the viewpoint of technological innovation, 5G service can be applied to other industries or developed further. Therefore, it is important to measure the demand of the Internet of things (IoT) because it is predicted to be commercialized widely in the 5G era and its demand hugely effects on the economic value of 5G industry. In this paper, we applied Bayesian method on regression model to find out the demand of 5G IoT service, wearable service in particular. As a result, we confirmed that the Bayesian regression model is closer to the actual value than the existing regression model. These findings can be utilized for predicting future demand of new industries.
Keywords
5G; Bayes' Theorem; Regression; Demand Forecast; IoT;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Aldrich, J., "R. A. Fisher on Bayes and Bayes' Theorem", Bayesian Analysis, Vol. 3. No. 1, 2008, pp. 161-170.   DOI
2 An, B. and Youn, K., "Sales Forecast Model for Stylish Goods", Business Management Review, Vol. 40, No. 1, 2007, pp. 41-52.
3 An, B., "Empirical Bayesian Demand Forecasting Model", Business Management Review, Vol. 42, No. 2, 2009, pp. 77-89.
4 Downey, A. B., Think Bayes, O'Reilly, 2014.
5 GSMA, The Mobile Economy 2017.
6 Hwang, K., Kim, W., and Jeong, C., "Demand Forecasting of Dok-do Tourism using Comparison of Univariate Time Series", Journal of Tourism & Leisure Research, Vol. 27, No. 2, 2015, pp. 59-77.
7 Jeon, C. and Lim, H., "Forecasting Seasonal Demands Using Bayesian Approach", Korean Management Science Review, Vol. 8, No. 2, 1991, pp. 25-35.
8 Jun, S., "Technology Forecasting using Bayesian Discrete Model", Journal of Korean Institute of Intelligent Systems, Vol. 27, No. 2, 2017, pp. 179-186.   DOI
9 Jung, H., Kim, S., and Song, K., "Weekly Maximum Electric Load Forecasting Method for 104 Weeks Using Multiple Regression Models", The Transactions of the Korean Institute of Electrical Engineers, Vol. 63, No. 9, 2014, pp. 1186-1191.   DOI
10 Kang, K., Bayesian Statistics, Free Academy Press, 2005.
11 KB Financial Group Management Research Institute, "Introduction of Monte-Carlo Method and Industrial and Financial Applications", KB Knowledge Vitamin, Vol. 17-31, 2017.
12 Kim, J., Cho, I., Kim, S., Lee, C., Kim, J., and Kim, H., 5G Socioeconomic Impact Analysis, KT Institute of Management Economics, 2018.
13 Park, K. and Kim, T., "A Study on the Diffusion Model of 5G Mobile Communication", JCCI 2018 Proceedings, 2018, pp. 33-34.
14 Kruschke, J. K., Doing Bayesian Data Analysis : A Tutorial with R, JAGS, and Stan, (2nd ed.), Academic Press, 2014.
15 Lee, K. and Jang, W., "Predicting Financial Success of a Movie Using Bayesian Choice Model", Proceedings of Spring Joint Conference of the Korean Institute of Industrial Engineers, 2006, pp. 1428-1433.
16 Lim, J. and Oh, H., "A Study on New Product Forecasting Methodology", Journal of the Korean Institute of Industrial Engineers, Vol. 18, No. 2, 1992, pp. 51-63.
17 Utterback, J. M., Mastering the Dynamics of Innovation, Harvard Business Press, 1996.