FORECASTING NUMBER OF INTERNATIONAL TOURIST ARRIVALS USING MULTI INPUT INTERVENTION ARIMA MODEL

  • Hidayatul Khusna Department of Statistics, Faculty of Sciences and Data Analytics, Institut Teknologi Sepuluh Nopember, Indonesia https://orcid.org/0000-0001-9889-2884
  • Muhammad Mashuri Department of Statistics, Faculty of Sciences and Data Analytics, Institut Teknologi Sepuluh Nopember, Indonesia
  • Muhammad Ahsan Department of Statistics, Faculty of Sciences and Data Analytics, Institut Teknologi Sepuluh Nopember, Indonesia
  • Wibawati Wibawati Department of Statistics, Faculty of Sciences and Data Analytics, Institut Teknologi Sepuluh Nopember, Indonesia
  • Diaz Fitra Aksioma Department of Statistics, Faculty of Sciences and Data Analytics, Institut Teknologi Sepuluh Nopember, Indonesia
  • Novri Suhermi Department of Statistics, Faculty of Sciences and Data Analytics, Institut Teknologi Sepuluh Nopember, Indonesia
Keywords: Covid-19, International Tourist Arrivals, Multi-Input Intervention, SARIMA

Abstract

In 2020, the Covid-19 pandemic caused a very significant impact resulting in the drastic decline in the number of international tourist visits. As the Covid-19 pandemic ends, the government reopen international flight to Indonesia in early 2022 to remark the revival of the tourism industry. To determine how big the impact of the Covid-19 pandemic as well as the recovery process on international tourist visits through Soekarno-Hatta, Ngurah-Rai, and Kualanamu airports in the coming period, forecasting is needed. The forecasting method utilized in this study is multi-input intervention analysis. The first input is caused by the outbreak of Covid-19 pandemic, while the second input is due to the international flight reopening. The type of intervention variable chosen is a step function because both inputs give permanent effect to the international tourist arrivals. The data used in this study are monthly international tourist arrivals based on the entrances to Soekarno-Hatta, Ngurah-Rai, and Kualanamu International Airports from January 2008 to September 2023, taken from the Central Bureau of Statistics website. Based on the results, it was found that the number of international tourist arrivals entering Soekarno-Hatta airport can be modelled using SARIMA (0,1,1)(0,1,0)12 with (b=2, s=1, r=0) and (b=2, s=[3], r=0) for first and second input of intervention variable, respectively. Furthermore, the number of international tourist visits through Ngurah-Rai airport was more appropriate to be modelled using SARIMA (1,1,1)(0,1,1)12 with intervention inputs (b=1, s=[2], r=0) and (b=4, s=0, r=1). In Kualanamu airport, the first intervention order is equal to that in Ngurah-Rai airport, with (b=3, s=[3], r=0) for second intervention input and SARIMA (0,1,1)(1,1,1)12 for pre-intervention data. The forecast results show that the number of international tourist arrivals entering Soekarno-Hatta, Ngurah-Rai, and Kualanamu international airports are already recovered to pre-pandemic conditions at a quick pace

Downloads

Download data is not yet available.

References

World Health Organization, “Coronavirus Disease (COVID-19),” https://www.who.int/health-topics/coronavirus#tab=tab_1.

J. H. Heslinga, M. Yusuf, J. Damanik, and M. Stokman, “Future strategies for tourism destination management: post COVID-19 lessons observed from Borobudur, Indonesia,” Journal of Tourism Futures, vol. 10, no. 1, pp. 68–74, 2024.

B.-S. Indonesia, “Statistik Kunjungan Wisatawan Mancanegara,” Bps, 2023.

P. W. Novianti and S. Suhartono, “PERMODELAN INDEKS HARGA KONSUMEN INDONESIA DENGAN MENGGUNAKAN MODEL INTERVENSI MULTI INPUT,” Buletin Ekonomi Moneter dan Perbankan, vol. 12, no. 1, 2010, doi: 10.21098/bemp.v12i1.350.

T. Woodfield, “Time series intervention analysis using SAS software,” in Proceedings of the Twelfth Annual SAS Users Group International Conference, 1987, pp. 331–339.

A. F. Sustrisno, Rais, and I. Setiawan, “Intervention Model Analysis The Number of Domestic Passengers at Sultan Hasanuddin Airports,” Parameter: Journal of Statistics, vol. 1, no. 1, 2021, doi: 10.22487/27765660.2021.v1.i1.15436.

G. E. P. Box and G. C. Tiao, “Intervention analysis with applications to economic and environmental problems,” J Am Stat Assoc, vol. 70, no. 349, 1975, doi: 10.1080/01621459.1975.10480264.

R. C. P. Chung, W. H. Ip, and S. L. Chan, “An ARIMA-intervention analysis model for the financial crisis in China’s manufacturing industry,” International Journal of Engineering Business Management, vol. 1, no. 1, 2009, doi: 10.5772/6785.

A. L. Schaffer, T. A. Dobbins, and S. A. Pearson, “Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions,” BMC Med Res Methodol, vol. 21, no. 1, 2021, doi: 10.1186/s12874-021-01235-8.

Y. Rashed, H. Meersman, E. Van De Voorde, and T. Vanelslander, “Short-term forecast of container throughout: An ARIMA-intervention model for the port of Antwerp oa,” Maritime Economics and Logistics, vol. 19, no. 4, 2017, doi: 10.1057/mel.2016.8.

J. Unnikrishnan and K. K. Suresh, “Modelling the Impact of Government Policies on Import on Domestic Price of Indian Gold Using ARIMA Intervention Method,” Int J Math Math Sci, vol. 2016, 2016, doi: 10.1155/2016/6382926.

H. Prabowo and I. R. Afandy, “Intervention Analysis and Machine Learning to Evaluate the Impact of COVID-19 on Stock Prices,” Inferensi, vol. 4, no. 1, 2021, doi: 10.12962/j27213862.v4i1.8626.

S. K. Prilistya, A. E. Permanasari, and S. Fauziati, “The Effect of the COVID-19 Pandemic and Google Trends on the Forecasting of International Tourist Arrivals in Indonesia,” in TENSYMP 2021 - 2021 IEEE Region 10 Symposium, 2021. doi: 10.1109/TENSYMP52854.2021.9550838.

E. Nuvitasari and S. H. W. Suhartono, “Analisis intervensi multi-input fungsi step dan pulse untuk peramalan kunjungan wisatawan ke Indonesia,” Pasca Sarjana Jurusan Statistik-FMIPA ITS, 2009.

D. T. Andariesta and M. Wasesa, “Machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic: a multisource Internet data approach,” Journal of Tourism Futures, 2022, doi: 10.1108/JTF-10-2021-0239.

M. O. Arshad, S. Khan, A. Haleem, H. Mansoor, M. O. Arshad, and M. E. Arshad, “Understanding the impact of Covid-19 on Indian tourism sector through time series modelling,” Journal of Tourism Futures, vol. 9, no. 1, 2023, doi: 10.1108/JTF-06-2020-0100.

D. Provenzano and S. Volo, “Tourism recovery amid COVID-19: The case of Lombardy, Italy,” Tourism Economics, vol. 28, no. 1, 2022, doi: 10.1177/13548166211039702.

A. Liu, L. Vici, V. Ramos, S. Giannoni, and A. Blake, “Visitor arrivals forecasts amid COVID-19: A perspective from the Europe team,” Ann Tour Res, vol. 88, 2021, doi: 10.1016/j.annals.2021.103182.

R. T. R. Qiu, D. C. Wu, V. Dropsy, S. Petit, S. Pratt, and Y. Ohe, “Visitor arrivals forecasts amid COVID-19: A perspective from the Asia and Pacific team,” Ann Tour Res, vol. 88, 2021, doi: 10.1016/j.annals.2021.103155.

N. Kourentzes et al., “Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team,” Ann Tour Res, vol. 88, 2021, doi: 10.1016/j.annals.2021.103197.

W. S. William, “Wei, 2006. Time Series Analysis: Univariate and Multivariate Methods.” Pearson Education Inc.

Published
2024-07-31
How to Cite
[1]
H. Khusna, M. Mashuri, M. Ahsan, W. Wibawati, D. Aksioma, and N. Suhermi, “FORECASTING NUMBER OF INTERNATIONAL TOURIST ARRIVALS USING MULTI INPUT INTERVENTION ARIMA MODEL”, BAREKENG: J. Math. & App., vol. 18, no. 3, pp. 1539-1548, Jul. 2024.