Abstract
INTRODUCTION: Tourism planning, particularly in rural areas, presents complex challenges due to the highly dynamic and interdependent nature of tourism demand, influenced by seasonal, geographical, and economic factors. Traditional tourism forecasting methods, such as ARIMA and Prophet, often rely on statistical models that are limited in their ability to capture long-term dependencies and multi-dimensional data interactions. These methods struggle with sparse and irregular data commonly found in rural tourism datasets, leading to less accurate predictions and suboptimal decision-making. METHODS: To address these issues, we propose NeuroTourism xLSTM, a neuro-inspired model designed to handle the unique complexities of rural tourism planning. Our model integrates an extended Long Short-Term Memory (xLSTM) framework with spatial and temporal attention mechanisms and a memory module, enabling it to capture both short-term fluctuations and long-term trends in tourism data. Additionally, the model employs a multi-objective optimization framework to balance competing goals such as revenue maximization, environmental sustainability, and socio-economic development. RESULTS: Experimental results on four diverse datasets, including ETT, M4, Weather2K, and the Tourism Forecasting Competition datasets, demonstrate that NeuroTourism xLSTM significantly outperforms traditional methods in terms of accuracy. DISCUSSION: The model's ability to process complex data dependencies and deliver precise predictions makes it a valuable tool for rural tourism planners, offering actionable insights that can enhance strategic decision-making and resource allocation.