Article: Intelligent automotive trade market forecasting and decision-making system: multi-source data fusion under meta-learning Journal: International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC) 2026 Vol.52 No.5 pp.13 - 24 Abstract: With the continuous advancement of intelligent vehicle technology, the smart automotive trade market has become increasingly complex and dynamic. This study proposes an intelligent forecasting framework that integrates multi-source data through a meta-learning-based approach. Specifically, a meta-attention gated recurrent unit (MA-GRU) model is developed to enhance the accuracy and robustness of market predictions. The model first extracts key automotive performance indicators and market-related features using a GRU network, and then applies an attention mechanism to capture the most informative temporal dependencies. To address the challenge of data scarcity in the emerging smart vehicle market, meta-learning is introduced to improve the model's adaptability and generalisation across diverse datasets. Experimental evaluations demonstrate that the proposed MA-GRU framework achieves superior predictive performance even under limited data conditions, providing a solid technical foundation for market trend analysis and strategic decision-making in the intelligent automotive trade. Inderscience Publishers - linking academia, business and industry through research

Title: Intelligent automotive trade market forecasting and decision-making system: multi-source data fusion under meta-learning

Authors: Kan Lu; Mingting Huang; Min Song

Addresses: School of Management, Jingzhou University, Jingzhou, 434020, China ' College of Management, Yangtze University College of Arts and Science, Jingzhou, 434000, China ' School of Management, Jingzhou University, Jingzhou, 434020, China

Abstract: With the continuous advancement of intelligent vehicle technology, the smart automotive trade market has become increasingly complex and dynamic. This study proposes an intelligent forecasting framework that integrates multi-source data through a meta-learning-based approach. Specifically, a meta-attention gated recurrent unit (MA-GRU) model is developed to enhance the accuracy and robustness of market predictions. The model first extracts key automotive performance indicators and market-related features using a GRU network, and then applies an attention mechanism to capture the most informative temporal dependencies. To address the challenge of data scarcity in the emerging smart vehicle market, meta-learning is introduced to improve the model's adaptability and generalisation across diverse datasets. Experimental evaluations demonstrate that the proposed MA-GRU framework achieves superior predictive performance even under limited data conditions, providing a solid technical foundation for market trend analysis and strategic decision-making in the intelligent automotive trade.

Keywords: intelligent marketing prediction; intelligent cars; gated recurrent unit; GRU; meta-learning.

DOI: 10.1504/IJAHUC.2026.154095

International Journal of Ad Hoc and Ubiquitous Computing, 2026 Vol.52 No.5, pp.13 - 24

Received: 04 Aug 2025
Accepted: 22 Dec 2025
Published online: 12 Jun 2026 *

Title: Intelligent automotive trade market forecasting and decision-making system: multi-source data fusion under meta-learning

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