TY - JOUR
T1 - Fuzzy time series and sarima model for forecasting tourist arrivals to Bali
AU - Elena, Maria
AU - Lee, Muhammad Hisyam
AU - Suhartono,
AU - Hossein,
AU - Haizum, Nur
AU - Bazilah, Nur Arina
PY - 2012
Y1 - 2012
N2 - Forecasting is very important in many types of organizations since predictions of future events must be incorporated into the decision-making process. In the case of tourism demand, better forecast would help directors and investors make operational, tactical, and strategic decisions. Generally, in time series we can divide forecasting method into classical method and modern methods. Although recent studies show that the newer and more advanced forecasting techniques tend to result in improved forecast accuracy under certain circumstances, no clear-cut evidence shows that any one model can consistently outperform other models in the forecasting competition [1]. In this study, the forecasting performance between Box-Jenkins approaches of seasonal autoregressive integrated moving average (SARIMA) and four models of fuzzy time series has been compared by using MAPE, MAD and RMSE as the forecast measures of accuracy. The empirical results show that Chen's fuzzy time series model outperforms the SARIMA and the other fuzzy time series models.
AB - Forecasting is very important in many types of organizations since predictions of future events must be incorporated into the decision-making process. In the case of tourism demand, better forecast would help directors and investors make operational, tactical, and strategic decisions. Generally, in time series we can divide forecasting method into classical method and modern methods. Although recent studies show that the newer and more advanced forecasting techniques tend to result in improved forecast accuracy under certain circumstances, no clear-cut evidence shows that any one model can consistently outperform other models in the forecasting competition [1]. In this study, the forecasting performance between Box-Jenkins approaches of seasonal autoregressive integrated moving average (SARIMA) and four models of fuzzy time series has been compared by using MAPE, MAD and RMSE as the forecast measures of accuracy. The empirical results show that Chen's fuzzy time series model outperforms the SARIMA and the other fuzzy time series models.
KW - Fuzzy time series
KW - SARIMA
UR - http://www.scopus.com/inward/record.url?scp=84872705276&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:84872705276
SN - 0127-9696
VL - 57
SP - 69
EP - 81
JO - Jurnal Teknologi (Sciences and Engineering)
JF - Jurnal Teknologi (Sciences and Engineering)
IS - SUPPL.1
ER -