A Data-Driven Intelligent System for Tourist Inflow Forecasting and Context-Aware Recommendation Using Time-Series and Machine Learning

Author(s)	Tejasvi Omkar, Sakshi Evane, Alok Sahu, Piyush Dhote, Shashank Mane
Country	India
Abstract	Tourism plays a crucial role in driving economic development and promoting cultural exchange worldwide. However, accurately predicting tourist inflow remains challenging due to dynamic factors such as seasonal patterns, weather conditions, and special events. This research presents an AI-driven predictive framework that integrates time-series analysis with machine learning techniques to estimate tourist arrivals and generate personalized travel recommendations. The proposed system utilizes historical tourism data, meteorological information, and event-based inputs to train models including Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks. Model performance is evaluated using standard error metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) to ensure reliability and precision. Additionally, a Flask-based web application is developed to visualize predicted travel trends and suggest optimal visiting periods. Experimental results indicate that the proposed approach significantly enhances the accuracy of tourist inflow prediction and contributes to the advancement of intelligent tourism systems.
Keywords	Smart Tourism, Machine Learning, Time-Series Analysis, LSTM Networks, Random Forest, XGBoost, Predictive Modeling, AI-Based Recommendation Systems.
Field	Engineering
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-07

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International Journal For Multidisciplinary Research