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

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 time series predictions and enhances interpretability. Firstly, the sliding window technique is applied to segment the original financial time series, with data in each window transformed into multiple feature representations. The features analyzed encompass price fluctuations, trading volumes, and technical indicators, enabling the model to identify intricate patterns within the series. Subsequently, the Gramian Angular Difference Fields (GADF) technique is employed to transform the extracted sequential features into image formats. Regarding model design, the MF-ConvLSTM network leverages both convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) networks. Additionally, a Fuzzy Control Mechanism (FCM) is integrated into the input gate of the ConvLSTM to facilitate subspace reconstruction, enhancing the information flow and state update process in the memory units. To enhance the model's interpretability, XAI technology is applied to feature importance analysis, decision path visualization, and model explanation reports. This helps researchers and practitioners better understand the model's decision-making process, increasing the model's transparency and credibility. To evaluate the effectiveness of the MF-ConvLSTM-XAI model, experiments are conducted using the New York Stock Exchange Composite Index dataset. The results indicate that MF-ConvLSTM-XAI achieves higher prediction accuracy compared with traditional LSTM models and other baseline methods. Specifically, the Mean Squared Error (MSE) on the test set decreases by approximately 15%, and the Mean Absolute Error (MAE) decreases by about 12%. Moreover, the model demonstrates greater robustness under highly volatile market conditions. These findings validate the effectiveness and advantages of MF-ConvLSTM-XAI in FTSF. Overall, by integrating multi-feature information and Fuzzy Cognitive Mechanisms (FCM), MF-ConvLSTM-XAI shows a positive impact on improving the accuracy and stability of financial time series predictions. Although the study achieves promising results on the New York Stock Exchange dataset, the model's generalization capability still requires further validation across more diverse financial datasets, including cryptocurrency and emerging markets. Nonetheless, this approach provides a valuable tool for financial data analysis and offers new insights for research and applications in the field.