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Title: A spatio-temporal transformer predictive model for elderly-oriented tourism via attention mechanism

Authors: Jiya Sun

Addresses: College of Management, Liaoning University of International Business and Economics, Dalian 116052, China

Abstract: To address the issue that current models for predicting the potential of retirement destinations overlook the spatio-temporal correlations between influencing factors, this paper first selects the influencing factors of retirement destination potential and designs an improved empirical mode decomposition algorithm to decompose these factors, obtaining the individual mode components. Then, the characteristics of each mode component are captured, and the spatio-temporal dependencies are unified through an adaptive embedding mechanism. Subsequently, a temporal self-attention module is designed to capture temporal dependencies, and a spatial self-attention mechanism is implemented to model geographical relationships. Feature fusion is achieved using a multi-head attention mechanism, and the prediction results are output through a feedforward neural network. Experimental outcome indicates that the prediction accuracy of the suggested model improves by 2.7%-11.8% compared to the baseline model, validating the superiority of the suggested model.

Keywords: potential prediction; spatiotemporal transformer; empirical mode decomposition; EMD; attention mechanism.

DOI: 10.1504/IJICT.2025.150143

International Journal of Information and Communication Technology, 2025 Vol.26 No.42, pp.82 - 98

Received: 12 Sep 2025
Accepted: 15 Oct 2025
Published online: 01 Dec 2025 *