Deep Learning With Processing Algorithms for Forecasting Tourist Arrivals

Abstract

The DL (Deep Learning) method is the standard for forecasting tourist arrivals. This method provides very good forecasting results but needs improvement if the data is small. Statistical data from the BPS (Central Bureau of Statistics) needs to be corrected, resulting in forecasts that tend to be invalid. This study uses statistical data and GT (Google Trends) as a solution so that the data is sufficient. GT data has a lot of noise because there is a shift between web searches and departures. This difference will produce noise that needs to be cleaned. We use monthly data from January 2008 to December 2021 from BPS sources combined with GT. Hilbert-Huang Transform (HHT) is proposed to clean data from various disturbances. The DL used in this study is long short-time memory (LSTM) and was evaluated using the root mean squared error RMSE and mean absolute percentage error (MAPE). The evaluation results show that the HHT-LSTM results are better than without data cleaning.