Forecasting the Number of Tourist Arrivals to Batam by applying the Singular Spectrum Analysis and the Arima Method
- DOI
- 10.2991/iconprocs-19.2019.24How to use a DOI?
- Keywords
- Singular Spectrum Analysis, Trend, Oscillatory, Noise, ARIMA
- Abstract
Singular Spectrum Analysis (SSA) is a time series method used to decompose the original time series into a sum of a small number of components that can be interpreted such as trends, oscillatory components, and noise. The purpose of this study is to compare the accuracy of the forecast between the SSA and ARIMA methods to obtain the best method in predicting the number of foreign tourist arrivals to Indonesia. The data used in this study is data on the number of arrivals of foreign tourists to Indonesia through the Batam entrance. The forecasting results obtained using the SSA method will be compared with the ARIMA method to assess its superiority. The level of forecasting accuracy generated by each method is measured by the criteria of Mean Absolute Percentage Error (MAPE). The results of the study show that the ARIMA method produces better forecast accuracy than the SSA method for forecasting the number of tourist arrivals through the Batam’s entrance. The MAPE value obtained from the results of forecasting using the ARIMA method is 9.83. The MAPE value obtained from the results of forecasting using the SSA method is 10.98.
- 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/).
Cite this article
TY - CONF AU - Agustinus Ete AU - Meita Fitrianawati AU - Muhammad Arifin PY - 2019/05 DA - 2019/05 TI - Forecasting the Number of Tourist Arrivals to Batam by applying the Singular Spectrum Analysis and the Arima Method BT - Proceedings of the First International Conference on Progressive Civil Society (ICONPROCS 2019) PB - Atlantis Press SP - 119 EP - 126 SN - 2352-5398 UR - https://doi.org/10.2991/iconprocs-19.2019.24 DO - 10.2991/iconprocs-19.2019.24 ID - Ete2019/05 ER -