학술논문

Forecasting Inbound Tourism in Uzbekistan: Leveraging AI and ARIMA Models for Economic Growth Insights
Document Type
Conference
Source
2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM) Computation, Automation and Knowledge Management (ICCAKM), 2023 4th International Conference on. :1-4 Dec, 2023
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Nuclear Engineering
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Economics
Analytical models
Biological system modeling
Computational modeling
Tourism industry
Predictive models
Artificial intelligence
Artificial Intelligence
Machine Learning
Uzbekistan Tourism
ARIMA Models
Predictive Analytic
Language
Abstract
Foreign tourists serve as one of the strong pillars in economic growth and prosperity for many countries globally. Uzbekistan is no exception, wherein the data suggests that it attracts substantial number of foreign tourists, specially post pandemic. It is observed that there are certain seasons in which the tourist's activity is at its peak. Looking at the data we see the seasonality in the trends. Motivated by this fact the current study tries to forecast the number of inbound tourists into Uzbekistan by utilising ML based modelling. Particularly we use the Auto Regressive Moving Average models to predict. Different models are tested and the best one picked to exhibit the forecast. The findings show the potential of AI and ML for predictive analytics in the tourism industry overall and provide useful information for anyone working in the Uzbek tourism sector. The present study paves the way for further investigation and practical application of cutting-edge methods for data analysis in strategic planning and decision-making.