Forecasting Tourist Arrivals in Nepal: A Comparative Analysis of Seasonal Models and Implications

Tourist arrivals play a vital role in the broader tourism ecosystem, substantially contributing to the economy. The global landscape has witnessed significant growth in international arrivals over the years, and Nepal has not been an exception to this trend, experiencing a steady influx of inbound tourists. Although tourist numbers are increasing, the lack of research in forecasting future arrivals is evident, highlighting the pressing need for comprehensive forecasting mechanisms to efficiently manage tourism resources and sustainably accommodate the growing influx of visitors. In order to gain insights into the dynamics of international tourist arrivals in Nepal, we conducted a comparative analysis using two distinct forecasting techniques: Seasonal Autoregressive Integrated Moving Average (SARIMA) and the Exponential Smoothing technique. Our analysis spanned from January 1992 to December 2023, enabling us to formulate forecasts for the upcoming months up to December 2030. The findings of our study underscore the suitability of all three models—namely, SARIMA, Winter Additive, and Winter Multiplicative—as effective tools for projecting international arrivals in Nepal. However, upon careful examination, the Winter Multiplicative model emerged as the most appropriate model for forecasting Nepal's international arrivals. This model aligned strongly with the observed data, enhancing its predictive accuracy. The implications of our research are far-reaching, offering valuable insights for various stakeholders within Nepal's tourism industry. These insights can guide tourism planners, policymakers, and other relevant entities in formulating well-informed strategies to strengthen and sustain the growth of the tourism sector in Nepal. As the nation continues to position itself on the global tourism map, equipped with data-driven forecasts, we believe that our study provides an essential resource for shaping the trajectory of Nepal's tourism industry in a positive direction.

Authors

Institutions

Publication Details

Journal
Journal of Statistical Theory and Applications
Published
2024-09-19
DOI
https://doi.org/10.1007/s44199-024-00079-7
Citations
4
Primary Topic
Forecasting Techniques and Applications
Type
article
Field-Weighted Citation Impact
1.96
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Forecasting Tourist Arrivals in Nepal: A Comparative Analysis of Seasonal Models and Implications

Tulsi Paudel, Thakur Dhakal, Wenya Li
4 citations
Journal of Statistical Theory and Applications
Forecasting Techniques and Applications
1.96
article

Forecasting Tourist Arrivals in Nepal: A Comparative Analysis of Seasonal Models and Implications

Tulsi Paudel, Thakur Dhakal, Wenya Li
article en
4 citations

Abstract

Tourist arrivals play a vital role in the broader tourism ecosystem, substantially contributing to the economy. The global landscape has witnessed significant growth in international arrivals over the years, and Nepal has not been an exception to this trend, experiencing a steady influx of inbound tourists. Although tourist numbers are increasing, the lack of research in forecasting future arrivals is evident, highlighting the pressing need for comprehensive forecasting mechanisms to efficiently manage tourism resources and sustainably accommodate the growing influx of visitors. In order to gain insights into the dynamics of international tourist arrivals in Nepal, we conducted a comparative analysis using two distinct forecasting techniques: Seasonal Autoregressive Integrated Moving Average (SARIMA) and the Exponential Smoothing technique. Our analysis spanned from January 1992 to December 2023, enabling us to formulate forecasts for the upcoming months up to December 2030. The findings of our study underscore the suitability of all three models—namely, SARIMA, Winter Additive, and Winter Multiplicative—as effective tools for projecting international arrivals in Nepal. However, upon careful examination, the Winter Multiplicative model emerged as the most appropriate model for forecasting Nepal's international arrivals. This model aligned strongly with the observed data, enhancing its predictive accuracy. The implications of our research are far-reaching, offering valuable insights for various stakeholders within Nepal's tourism industry. These insights can guide tourism planners, policymakers, and other relevant entities in formulating well-informed strategies to strengthen and sustain the growth of the tourism sector in Nepal. As the nation continues to position itself on the global tourism map, equipped with data-driven forecasts, we believe that our study provides an essential resource for shaping the trajectory of Nepal's tourism industry in a positive direction.

Journal of Statistical Theory and ApplicationsVol. 23(3)
Sanming University (CN), Yeungnam University (KR)
Openalex Percentile: Top 13%
Forecasting Techniques and Applications
1.96
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.