FORECASTING TOURISM DEMAND DURING THE COVID-19 PANDEMIC: ARIMAX AND INTERVENTION MODELLING APPROACHES
Abstract
The tourism sector in Indonesia is one of the economic sectors severely impacted by the COVID-19 epidemic and its variants. The government is attempting to revive the economy by implementing numerous recovery policies. These economic recovery policies, particularly in the tourism sector, must be backed by a policy evaluation conducted using tourism demand data, such as the number of international visitors visiting Indonesia. However, official data from the BPS-Statistics Indonesia has been released with a two-month delay and is sometimes revised the following month. Consequently, it is necessary to forecast the data for the current situation. In addition, the forecasting model must be modified to account for data conditions caused by the COVID-19 pandemic. This research proposes an ARIMAX forecasting model that utilizes Google Trends and an intervention forecasting model with data sources from BPS and Google Trends. Thus, this research aims to present an overview, develop a model and identify the best forecasting model, and calculate the influence of the intervention on the number of international visitors visiting Indonesia. Compared to the ARIMAX model, the results indicated that the intervention model provided the most accurate forecasts. Not only superior in forecasting results, but the intervention model also demonstrates the magnitude of the intervention's effect on the number of international visitors visiting Indonesia.
Downloads
References
I. G. B. Arjana, Geografi Pariwisata dan Ekonomi Kreatif. Jakarta: Rajawali Pers, 2016.
Kemenparekraf/Baparekraf, “Rencana Strategis Kementerian Pariwisata dan Ekonomi Kreatif/Badan Pariwisata dan Ekonomi Kreatif 2020-2024,” Jakarta, 2020.
BPS, International Visitor Arrivals Statistics 2020. Jakarta: BPS, 2021. Accessed: Oct. 22, 2021. [Online]. Available: https://www.bps.go.id/publication/2021/06/30/ddea1823bc9cd63789d51b05/statistik-kunjungan-wisatawan-mancanegara-2020.html
WTTC, “INDONESIA 2022 Annual Research: Key Highlights,” 2022. Accessed: Aug. 22, 2022. [Online]. Available: https://wttc.org/Research/Economic-Impact
UNWTO, “Covid-19 and Tourism 2020: A year in review,” 2020. [Online]. Available: https://www.unwto.org/covid-19-and-tourism-2020
Kemenko Perekonomian, “Siaran Pers: Pemerintah Dorong Pemulihan Sektor Pariwisata dan Ekonomi Kreatif,” Sep. 27, 2021. https://ekon.go.id/publikasi/detail/3332/pemerintah-dorong-pemulihan-sektor-pariwisata-dan-ekonomi-kreatif
E. Cebrián and J. Domenech, “Is Google Trends a quality data source?,” Appl Econ Lett, pp. 1–5, Jan. 2022, doi: 10.1080/13504851.2021.2023088.
V. Z. Eichenauer, R. Indergand, I. Z. Martínez, and C. Sax, “Obtaining consistent time series from Google Trends,” Econ Inq, vol. 60, no. 2, pp. 694–705, Apr. 2022, doi: 10.1111/ecin.13049.
B. Bokelmann and S. Lessmann, “Spurious patterns in Google Trends data - An analysis of the effects on tourism demand forecasting in Germany,” Tour Manag, vol. 75, pp. 1–12, Dec. 2019, doi: 10.1016/j.tourman.2019.04.015.
W. Höpken, T. Eberle, M. Fuchs, and M. Lexhagen, “Improving Tourist Arrival Prediction: A Big Data and Artificial Neural Network Approach,” J Travel Res, vol. 60, no. 5, pp. 998–1017, May 2021, doi: 10.1177/0047287520921244.
L. Wen, C. Liu, and H. Song, “Forecasting tourism demand using search query data: A hybrid modelling approach,” Tourism Economics, vol. 25, no. 3, pp. 309–329, May 2019, doi: 10.1177/1354816618768317.
G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time Series Analysis: Forecasting and Control, Fifth. Hoboken, New Jersey: Wiley, 2016.
L. Budiarti, Tarno, and B. Warsito, “ANALISIS INTERVENSI DAN DETEKSI OUTLIER PADA DATA WISATAWAN DOMESTIK (Studi Kasus di Daerah Istimewa Yogyakarta),” JURNAL GAUSSIAN, vol. 2, no. 1, pp. 39–48, 2013, doi: 10.14710/j.gauss.v2i1.2742.
D. al Mahkya and D. Anggraini, “Forecasting the Number of Passengers from Bakauheni Port during the Sunda Strait Tsunami Period Using Intervention Analysis Approach and Outlier Detection,” IOP Conf Ser Earth Environ Sci, vol. 537 012009, pp. 1–9, Jul. 2020, doi: 10.1088/1755-1315/537/1/012009.
M. Hisyam Lee, Suhartono, and B. Sanugi, “Multi Input Intervention Model for Evaluating the Impact of the Asian Crisis and Terrorist Attacks on Tourist Arrivals,” 2010. [Online]. Available: https://oarep.usim.edu.my/jspui/handle/123456789/4216
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,” Maritime Economics & Logistics, vol. 19, no. 4, pp. 749–764, Dec. 2017, doi: 10.1057/mel.2016.8.
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, pp. 13–27, Mar. 2021, doi: 10.12962/j27213862.v4i1.8626.
R. Mulero and A. García-Hiernaux, “Forecasting Spanish unemployment with Google Trends and dimension reduction techniques,” SERIEs, vol. 12, no. 3, pp. 329–349, Sep. 2021, doi: 10.1007/s13209-021-00231-x.
S. J. Page, Tourism Management, Sixth. New York: Routledge, 2019.
J. Fletcher, A. Fyall, D. Gilbert, and S. Wanhill, Tourism: Principles and Practice, Sixth. Harlow: Pearson, 2018.
E. L. Rödel, “Forecasting tourism demand in Amsterdam with Google Trends: A research into the forecasting potential of Google Trends for tourism demand in Amsterdam,” University of Twente, 2017. [Online]. Available: http://essay.utwente.nl/73929/
B. H. Andrews, M. D. Dean, R. Swain, and C. Cole, “Building ARIMA and ARIMAX Models for Predicting Long-Term Disability Benefit Application Rates in the Public/Private Sectors,” 2013. [Online]. Available: https://www.soa.org/resources/research-reports/2013/research-2013-arima-arimax-ben-appl-rates/
R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, Third. Melbourne: OTexts, 2021. [Online]. Available: https://otexts.com/fpp3/
S. Gössling, D. Scott, and C. M. Hall, “Pandemics, tourism and global change: a rapid assessment of COVID-19,” Journal of Sustainable Tourism, vol. 29, no. 1, pp. 1–20, Jan. 2021, doi: 10.1080/09669582.2020.1758708.
Wardiyanta, Pengantar Ekonomi Pariwisata. Yogyakarta: Pustaka Pelajar, 2020.
M. Henseler, H. Maisonnave, and A. Maskaeva, “Economic impacts of COVID-19 on the tourism sector in Tanzania,” Annals of Tourism Research Empirical Insights, vol. 3, no. 1, p. 100042, May 2022, doi: 10.1016/j.annale.2022.100042.
Copyright (c) 2023 Fahriza Rianda, Hardius Usman
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.
