TOURISM DEMAND FORECASTING IN MALAYSIA USING SARIMA AND LONG SHORT-TERM MEMORY (LSTM) MODEL
DOI:
https://doi.org/10.21837/pm.v23i39.1914Keywords:
Tourism Demand Forecasting, Time Series, Seasonal Autoregressive Moving Average, Long Short-Term MemoryAbstract
This study investigates a robust forecasting framework for predicting Malaysia’s international tourist arrivals by comparing the performance of a classical Seasonal Autoregressive Integrated Moving Average (SARIMA) model and a deep learning-based Long Short-Term Memory (LSTM) model. Unlike previous studies that applied these models individually, this research provides a direct empirical comparison using recent historical data on international tourist arrivals to evaluate their suitability for Malaysia’s tourism context. Model accuracy was evaluated using two statistical performance metrics. The results demonstrate that both models effectively capture tourism demand patterns, with SARIMA achieving superior forecasting accuracy (MAPE = 5.31%). The findings offer new empirical evidence on model performance in tourism demand forecasting and provide practical insights to support data-driven decision-making and strategic planning within Malaysia’s tourism sector.
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