A Comparative Study of AutoSARIMAX and Long Short-Term Memory Models for Tourist Arrival Forecasting - Saptarini, Dian; Saputri, Dian Syafitri Chani; Wardhana, Helna; Martono, Galih Hendro (Universitas Bumigora, 2026)
This study aims to predict the number of tourist arrivals in West Nusa Tenggara (NTB) Province using two forecasting approaches: AutoRegressive Integrated Moving Average with Exogenous Variables (AutoSARIMAX) and Long Short-Term Memory (LSTM). The dataset was obtained from the Central Bureau of Statistics (BPS) of NTB and consists of international and domestic tourist arrivals and monthly inflation rates for the period 2014–2023. The research process includes data collection, preprocessing, model construction, and result evaluation. The AutoSARIMAX model is applied to capture linear relationships with exogenous variables, while LSTM is employed to model long-term nonlinear patterns. The findings reveal that the LSTM model achieved better forecasting performance, with a Mean Absolute Percentage Error (MAPE) of 2.65%, which is lower than AutoSARIMAX with 3.25%. Nevertheless, AutoSARIMAX provides valuable interpretability regarding the influence of inflation on tourist arrivals. Overall, the comparison between the two models indicates that LSTM is more effective for time-series forecasting of tourist arrivals, while AutoSARIMAX remains useful for analyzing causal relationships. These insights can support decision-making in tourism planning, particularly in anticipating fluctuations driven by economic and external factors.