Enhancing Tourism Demand Forecasting Accuracy Through Clustering Time Series: A Comparison MAPE Analysis of Indonesian Provincial Domestic Tourist Flows
DOI:
https://doi.org/10.21512/emacsjournal.v7i3.14112Keywords:
Tourism, Forecasting, Geometric Brownian Motion, ClusteringAbstract
The post-pandemic recovery period of the Indonesian tourism sector poses new challenges for accurate tourism demand forecasting across Indonesia's diverse provincial richness. This research aims to enhance the predictive accuracy of domestic tourism demand by comparing conventional single-provincial forecasting methods with clustering-based time series techniques. The Geometric Brownian Motion (GBM) model analyzed data regarding the monthly influx of domestic tourists to 34 provinces from January 2021 to May 2025. This study utilized average linkage agglomerative nesting (AGNES) clustering to discern structural similarities among provinces. Subsequently, silhouette analysis was employed to determine the optimal number of clusters. The findings demonstrate that the cluster-based forecasting approach markedly improved accuracy relative to the non-clustered model. The Mean Absolute Percentage Error (MAPE) for the traditional provincial forecasts was 16.48%. The first cluster-based model had an MAPE of 13.38% and the second cluster-based model had an MAPE of 6.54%. These findings indicate that grouping provinces with analogous temporal patterns enhances the model's ability to identify the underlying dynamics in domestic tourism flows. The work underscores the efficacy of combining stochastic models with hierarchical clustering to enhance evidence-based tourist planning and policy development. This study improves sustainable tourism management by providing an empirical foundation for enhanced forecasting precision, particularly in post-crisis recovery periods.
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