International trade market forecasting and decision-making system: multimodal data fusion under meta-learning.

ABSTRACT: Traditional market analysis tools primarily rely on unidimensional data, such as historical trading records and price trends. However, these data are often insufficient to reflect the actual state of the market fully. This study introduces a meta-learning-based (MLB) multimodal data fusion approach to optimize feature extraction and fusion strategies, addressing the complexity and heterogeneity inherent in international trade market data. Initially, the mel-frequency cepstral coefficients (MFCC) method is employed to transform the original audio signal into more discriminative spectral features. For image data, the convolutional block attention module (CBAM) is incorporated to capture both channel-wise and spatial attention, thereby improving the model's ability to focus on market-relevant

SUBMITTER: Bai Y 

PROVIDER: S-EPMC12453693 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature