Tourist Flow, Economic Impact, and Machine Learning-Based Forecasting of Tourism in Jammu & Kashmir Union Territory: A Comprehensive Empirical Analysis (2000–2032)

Imtiaz Ahmed

doi:10.36948/ijfmr.2026.v08i02.73584

Tourist Flow, Economic Impact, and Machine Learning-Based Forecasting of Tourism in Jammu & Kashmir Union Territory: A Comprehensive Empirical Analysis (2000–2032)

Author(s)	Dr. Imtiaz Ahmed
Country	India
Abstract	Tourism constitutes the economic backbone of Jammu & Kashmir Union Territory (J&K UT), yet rigorous quantitative analysis of its long-run dynamics, structural breaks, and predictive trajectory remains scarce in the scholarly literature. This study assembles a comprehensive 25-year annual tourist-arrival dataset for J&K UT (2000–2024), disaggregated into domestic and foreign arrivals, stratified by the Jammu and Kashmir sub-regions, and augmented by pilgrimage-specific data. The dataset is subjected to full descriptive statistics, Augmented Dickey–Fuller (ADF) unit-root tests, Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analysis, and endogenous structural break identification via the Zivot–Andrews test. A six-model machine learning forecasting experiment compares ARIMA(2,1,2), Random Forest, XGBoost, Support Vector Regression, Long Short-Term Memory (LSTM) networks, and an LSTM–Prophet hybrid ensemble, evaluated on a demanding test window encompassing the COVID-19 collapse and post-pandemic rebound (2020–2024). The LSTM–Prophet hybrid emerges as the superior architecture with MAPE 7.9% and R² = 0.942, against ARIMA's MAPE of 24.6%. Shapley value decomposition identifies geopolitical security events (31.3%), lagged arrivals (42.1%), and connectivity improvements (8.4%) as the dominant drivers of arrival variability. Scenario-conditioned forecasts project J&K UT domestic arrivals at 2.87 crore (baseline) to 3.38 crore (optimistic) by 2027, and 4.45–5.35 crore by 2032. The study contributes the first ML-powered, statistically rigorous analysis of post-reorganisation J&K UT tourism and delivers evidence-based policy recommendations for sustainable tourism governance in a conflict-proximate Himalayan destination.
Keywords	Jammu & Kashmir tourism; tourist arrivals forecasting; LSTM neural network; ARIMA; structural breaks; machine learning; Vaishno Devi; Kashmir Valley; economic impact; scenario forecasting
Field	Sociology > Tourism / Transport
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-05
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.73584

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Tourist Flow, Economic Impact, and Machine Learning-Based Forecasting of Tourism in Jammu & Kashmir Union Territory: A Comprehensive Empirical Analysis (2000–2032)

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