New AI model forecasts tourist arrivals using social influence weighting

A new research paper proposes a hybrid approach for forecasting tourist arrivals, integrating Internet big data with a game-theoretic algorithm to weight social media opinion leaders. The method uses Empirical Wavelet Transform to manage nonstationary attributes and a Stacked Bi-directional Long Short-Term Memory network, optimized by Bayesian methods, for generating forecasts. This approach demonstrated a significant reduction in root mean squared error compared to traditional methods, showing robustness even under volatile conditions. AI

IMPACT This novel approach could enhance planning and logistics in service systems by improving the accuracy of demand forecasting.

RANK_REASON Research paper published on arXiv detailing a novel forecasting methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Ali Nikseresht
• Bayesian optimization
• China
• COVID-19
• Empirical Wavelet Transform Based Features for Classification of Parkinson's Disease Severity.
• Fifth Industrial Revolution
• Hong Kong
• Sanya Hainan
• Shapley Values
• Stacked Bi-directional Long Short-Term Memory

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