TIC-FusionNet: A multimodal deep learning framework with temporal decomposition and attention-based fusion for time series forecasting.

ABSTRACT: We propose TIC-FusionNet, a trend-aware multimodal deep learning framework for time series forecasting with integrated visual signal analysis, aimed at addressing the limitations of unimodal and short-range dependency models in noisy financial environments. The architecture combines Exponential Moving Average (EMA) decomposition for denoising and trend extraction, a lightweight Linear Transformer for efficient long-sequence temporal modeling, and a spatial-channel CNN with CBAM attention to capture morphological patterns from candlestick chart images. A gated fusion mechanism adaptively integrates numerical and visual modalities based on context relevance, enabling dynamic feature weighting under varying market conditions. We evaluate TIC-FusionNet on six real-world stock datasets, includi

SUBMITTER: Chen L 

PROVIDER: S-EPMC12510716 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature