Residual-Based Hybrid SARIMA–LSTM for Bali Tourism Demand Forecasting Using Google Trends

Authors

  • Junaedi Buddhi Dharma University, Indonesia
  • Aditiya Hermawan Buddhi Dharma University, Indonesia
  • Yusuf Kurnia Buddhi Dharma University, Indonesia
  • Ardiane Rossi Kurniawan Maranto Buddhi Dharma University, Indonesia
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DOI:

https://doi.org/10.63158/journalisi.v8i3.1644

Keywords:

Bali tourism demand forecasting, Google TrendsSARIMA–LSTM, Residual learning, Search Query Data, Tourism Analytics

Abstract

Accurate tourism demand forecasting is essential for destinations characterized by strong seasonality, nonlinear fluctuations, and post-pandemic recovery uncertainty. This study develops a residual-based hybrid SARIMA–LSTM model for forecasting monthly international tourist arrivals to Bali, Indonesia, using historical arrival data and Google Trends search query data. The dataset covers January 2009 to December 2024, comprising 192 monthly observations. A chronological split was applied, with January 2009 to December 2022 used for training and January 2023 to December 2024 used for testing. SARIMA was employed to capture linear and seasonal structures, while LSTM was used to learn nonlinear residual patterns. The proposed model was compared with SARIMA, Random Forest, standalone LSTM, and SARIMA–RF using RMSE, MAPE, and R². The SARIMA–LSTM model achieved the best performance, with RMSE = 35,915.36, MAPE = 5.64%, and R² = 0.68, compared with SARIMA, which obtained RMSE = 37,052.68, MAPE = 5.70%, and R² = 0.65. These findings indicate that residual-based hybridisation provides incremental forecasting improvement. However, the independent contribution of Google Trends is not separately isolated in this study and should therefore be interpreted cautiously as a complementary behavioural signal within the proposed forecasting framework.

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Published

2026-06-27

Issue

Vol. 8 No. 3 (2026): June

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Articles

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