Comparative Evaluation of Forecasting Methods for Tourist Arrival Prediction: A Sliding Window-Based Analysis

Ahmad Ashril Rizal

doi:10.31154/cogito.v11i1.947.152-166

Authors

Ahmad Ashril Rizal Universitas Islam Negeri Mataram

DOI:

https://doi.org/10.31154/cogito.v11i1.947.152-166

Keywords:

Prediction, Sliding Window, Sarima, Prophet, Xgboost, Tourism

Abstract

The tourism sector in West Nusa Tenggara (NTB) plays a strategic role in driving regional economic growth. However, its management still faces challenges in data-driven planning, particularly in accurately forecasting tourist arrivals. This issue is further complicated by the seasonal and volatile nature of tourist visit patterns, which are highly susceptible to external disruptions such as pandemics. This study initiates the development of a deep learning-based forecasting system to support the implementation of smart tourism in NTB. This study evaluates and compares the performance of three time series forecasting methods—SARIMA, Prophet, and XGBoost—using a sliding window approach to assess the temporal stability of their predictive performance. The analysis uses monthly international tourist arrival data from 2010 to 2024. The experimental results reveal that the SARIMA(1,0,2)(0,1,1,12) model provides the most stable accuracy, with an average MAPE of 35.22%, making it suitable for macro-level planning. The XGBoost model achieved the lowest MAPE of 29.84%, although it exhibited greater variability across windows. In contrast, the Prophet model demonstrated high sensitivity to data anomalies, particularly during the pandemic period. These findings suggest that classical statistical models like SARIMA remain relevant in handling periodic and limited datasets but have limitations in capturing complex patterns that may be better modeled through deep learning approaches.

References

R. Zhang, R. Zhao, and F. Suo, “Forecast Of International Tourist Arrivals Based On Arima Model,” in 2023 IEEE 6th International Conference on Information Systems and Computer Aided Education, ICISCAE 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 492–496. doi: 10.1109/ICISCAE59047.2023.10393516.

T. Makoni, G. Mazuruse, and B. Nyagadza, “International Tourist Arrivals Modelling and Forecasting: A Case Of Zimbabwe,” Sustainable Technology and Entrepreneurship, vol. 2, no. 1, Jan. 2023, doi: 10.1016/j.stae.2022.100027.

A. A. Rizal, S. Soraya, and M. Tajuddin, “Sequence to Sequence Analysis with Long Short Term Memory for Tourist Arrivals Prediction,” J Phys Conf Ser, vol. 1211, no. 1, 2019, doi: 10.1088/1742-6596/1211/1/012024.

Apudin, Gunawan, B. Sugiarto, R. H. Laluma, Gunawansyah, and T. Wiharko, “Implementation of Artificial Neural Network (ANN) for Prediction of Tourist Arrivals in Pamoyanan Hill,” in Proceeding of 2024 18th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2024, Institute of Electrical and Electronics Engineers Inc., 2024. doi: 10.1109/TSSA63730.2024.10864361.

B. Hidayaturrohman and W. Anggraeni, “Exploring CNN-BILSTM in Forecasting Tourist Arrivals,” in 2024 International Conference on Information Technology Systems and Innovation, ICITSI 2024 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2024, pp. 125–130. doi: 10.1109/ICITSI65188.2024.10929294.

P. K. Sinha, Image Acquisition and Preprocessing for Machine Vision Systems. Washington: SPIE Press, 2012.

J. Wang, P. Ge, and Z. Liu, “Using Denoised LSTM Network for Tourist Arrivals Prediction,” in 2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning, PRML 2021, Institute of Electrical and Electronics Engineers Inc., Jul. 2021, pp. 176–182. doi: 10.1109/PRML52754.2021.9520695.

C. Liu, Z. Jin, J. Gu, and C. Qiu, “Short-Term Load Forecasting Using a Long Short-Term Memory Network,” in 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), IEEE, Sep. 2017, pp. 1–6. doi: 10.1109/ISGTEurope.2017.8260110.

P. Kaewmanee, J. Muangprathub, and W. Sae-Jie, “Forecasting Tourist Arrivals with Keyword Search Using Time Series,” in ECTI-CON 2021 - 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology: Smart Electrical System and Technology, Proceedings, Institute of Electrical and Electronics Engineers Inc., May 2021, pp. 171–174. doi: 10.1109/ECTI-CON51831.2021.9454824.

C. Li, P. Ge, Z. Liu, and W. Zheng, “Forecasting Tourist Arrivals Using Denoising and Potential Factors,” Ann Tour Res, vol. 83, Jul. 2020, doi: 10.1016/j.annals.2020.102943.

T. Lingyu, W. Jun, and Z. Chunyu, “Mode Decomposition Method Integrating Mode Reconstruction, Feature Extraction, and Elm for Tourist Arrival Forecasting,” Chaos Solitons Fractals, vol. 143, Feb. 2021, doi: 10.1016/j.chaos.2020.110423.

E. Zhao, P. Du, and S. Sun, “Historical Pattern Recognition with Trajectory Similarity for Daily Tourist Arrivals Forecasting,” Expert Syst Appl, vol. 203, Oct. 2022, doi: 10.1016/j.eswa.2022.117427.

S. Siami-Namini, N. Tavakoli, and A. Siami Namin, “A Comparison of Arima and LSTM in Forecasting Time Series,” in Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, Institute of Electrical and Electronics Engineers Inc., Jul. 2018, pp. 1394–1401. doi: 10.1109/ICMLA.2018.00227.

S. Junaidi et al., Buku Ajar Machine Learning. Jambi: Sonpedia, 2024.

S. Rifky et al., Artificial Intelligence (Teori Dan Penerapan AI di Berbagai Bidang). Jambi: PT. Sonpedia Publishing Indonesia, 2024.

S. J. Russell and P. Norvig, Artificial Intelligence A Modern Approach Third Edition. New Jersey: Prantice Hall, 2010.

Badan Pariwisata dan Ekonomi Kreatif, “Laporan Kinerja Kementerian Pariwisata dan Ekonomi Kreatif / Badan Pariwisata dan Ekonomi Kreatif,” Jakarta, 2023.

Badan Pusat Statistik Provinsi NTB, “Provinsi Nusa Tenggara Barat Dalam Angka,” Mataram, 2023.

A. A. Rizal and S. Soraya, “Multi Time Steps Prediction Dengan Recurrent Neural Network Long Short Term Memory,” Matrik, vol. 18, no. 1, pp. 115–124, 2018.

A. A. Rizal and S. Hartati, “Recurrent Neural Network with Extended Kalman Filter for Prediction of The Number of Tourist Arrival in Lombok,” in 2016 International Conference on Informatics and Computing (ICIC), IEEE, 2016, pp. 180–185. doi: 10.1109/IAC.2016.7905712.