A MF-ConvLSTM-XAI model integrating multi-feature and fuzzy control for financial time series forecasting.

ABSTRACT: In financial time series prediction, existing methods struggle to fully capture the dynamic characteristics and nonlinear relationships of the data. Furthermore, they lack sufficient capability to integrate multiple features (such as prices, trading volumes, and technical indicators), particularly in highly volatile market conditions, where the prediction accuracy and stability of these models tend to be poor. This study proposes a financial time series forecasting (FTSF) model named Multi-Feature Convolutional Long Short-Term Memory Network-Explainable Artificial Intelligence (MF-ConvLSTM-XAI). By deeply integrating Convolutional Long Short-Term Memory Networks (ConvLSTM) with Explainable Artificial Intelligence (XAI) techniques, the model improves the accuracy and stability of financial

SUBMITTER: Liu R 

PROVIDER: S-EPMC12669620 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature