The number of employed people and tourist arrival in Malaysia using ARIMA and Fuzzy Time Series model: pre, during and post COVID-19 / Siti Norashikin Roslan and Siti Fatimah Abd Rahman - Roslan, Siti Norashikin; Abd Rahman, Siti Fatimah (College of Computing, Informatics and Media, UiTM Perlis, 2023)
Covid-19 has cause enormous challenge to Malaysia when this pandemic has lowered the tourism demand and cause the number of tourist arrival in Malaysia to decrease from 26.1 million in 2019 to 4.3 million in 2020. Many workers have also been laid off by their company due to the in capabilities of the business to generate revenues to pay their workers. Forecasting the number of tourist arrivals and the number of employed people is studied and correlation between the two figures are calculated in order to overcome the problems. The main objectives this paper aims to achieve are to find the relationship between the number of employed people and the number of tourist arrival in Malaysia and in finding the forecasted values for both data sets. The aim in finding the relationship between these data is to determine whether the number of tourist arrivals affects the number of employed people or otherwise. The data sets used were from the Tourism Malaysia website, CEIC data and Department of Statistics Malaysia (DOSM) dating from January 2018 until September 2022. ARIMA and Fuzzy Time Series methods are chosen to find the forecast value while the correlation regression is to assist in finding the correlation. MSE, RMSE and MAPE were also utilized to compare the error measures gathered between the two methods. The result shows that ARIMA (2,1,0) is the best method to forecast the number of employed people while Fuzzy Time Series is better for the number of tourist arrivals. However, the correlation values calculated suggested strong relationship only during the endemic phase.