Predicting the Participation in Social Science under Expanding System by Using ARIMAX

DOI

10.2991/mmsta-19.2019.32How to use a DOI?

Keywords

ARIMA; ARIMAX; cross correlation function; higher education; social science; transfer function

Abstract

This study aims to predict the participation pattern related to the social science programs within a high participated higher education system. Taking the expanding higher education system in Taiwan as an example, we collected the series data with student numbers in social science programs and total student enrollment numbers by using the annual statistics report (1950 to 2017) from Ministry of Education. Considered the concurrent series did not fit the classical ARIMA (autoregressive integrated moving average) model, this study selected transfer function in terms of multivariate autoregressive integrated moving average (ARIMAX) models to deal with the target series. First, we applied the cross correlation function to check the relationships between the series. Second, we select the ARIMAX with transfer function to verify the fittest predicting model. The result reveals the selected ARIMAX(1,1,1) model works well for predicting the trend of social science participation in future. This study provides an example to tackle two series variables in ARIMAX process in higher education settings. The finding suggests useful information for related policy makers to renovating the social science programs.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Dian-Fu Chang
AU  - Chia-Chi Chen
PY  - 2019/12
DA  - 2019/12
TI  - Predicting the Participation in Social Science under Expanding System by Using ARIMAX
BT  - Proceedings of the 2019 2nd International Conference on Mathematics, Modeling and Simulation Technologies and Applications (MMSTA 2019)
PB  - Atlantis Press
SP  - 152
EP  - 156
SN  - 2352-538X
UR  - https://doi.org/10.2991/mmsta-19.2019.32
DO  - 10.2991/mmsta-19.2019.32
ID  - Chang2019/12
ER  -