Proceedings of 2016 5th International Conference on Social Science, Education and Humanities Research

Mathematical Forecasting of the Tourism Activity Importance in Chinese Economy based on Holt's Exponential Smoothing Model

Authors
JuXing Wang
Corresponding Author
JuXing Wang
Available Online July 2016.
DOI
10.2991/ssehr-16.2016.314How to use a DOI?
Keywords
Tourism Activities, Forecasting of Economy, RMSE, Holt's Model
Abstract

As economy grows, the tourism shares a more and more important part in the development. This paper aims to forecast the importance of the tourism activities in Chinese economy. We got the initial values of forecast by linear regression based on the data on percentage of tourism industry in terms of employment from 1988 to 2015, and used Holt's exponential smoothing model to get the optimal RMSE. Finally, we get the predicted values from 2016 to 2018 with minimized RMSE.

Copyright
© 2016, 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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Volume Title
Proceedings of 2016 5th International Conference on Social Science, Education and Humanities Research
Series
Advances in Social Science, Education and Humanities Research
Publication Date
July 2016
ISBN
10.2991/ssehr-16.2016.314
ISSN
2352-5398
DOI
10.2991/ssehr-16.2016.314How to use a DOI?
Copyright
© 2016, 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/).

Cite this article

TY  - CONF
AU  - JuXing Wang
PY  - 2016/07
DA  - 2016/07
TI  - Mathematical Forecasting of the Tourism Activity Importance in Chinese Economy based on Holt's Exponential Smoothing Model
BT  - Proceedings of 2016 5th International Conference on Social Science, Education and Humanities Research
PB  - Atlantis Press
SP  - 1473
EP  - 1477
SN  - 2352-5398
UR  - https://doi.org/10.2991/ssehr-16.2016.314
DO  - 10.2991/ssehr-16.2016.314
ID  - Wang2016/07
ER  -