March 3, 2026Open Access

Intelligent Automotive Trade Market Forecasting and Decision-Making System: Multi-Source Data Fusion under Meta-Learning

Key Points

Forecasting systems show improved accuracy in predicting market trends, optimizing trade decisions.
Key evidence includes multi-source data fusion techniques that boost the forecasting process.
Analysis of automotive trade market data employs meta-learning principles to enhance decision-making capabilities.
Highlighting the need for sophisticated data integration strategies to support sustainable development in automotive sectors.

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

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Authors

Kan Lu

Mingting Huang

Min Song

Journals

International Journal of Ad Hoc and Ubiquitous Computing

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Intelligent Automotive Trade Market Forecasting and Decision-Making System: Multi-Source Data Fusion under Meta-Learning

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Authors

Journals

Actions

References and Citations

Citation Network

Connected Papers

Discussion

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