## 3 FINM Prediction Model  
When dealing with complex and high-dimensional time series data, it is often difficult for a single model to achieve an ideal balance between prediction accuracy and computational efficiency. Based on this challenge, this study analyzed in depth the advantages and limitations of existing models such as CNNs, BiLSTMs, and GRUs. By comprehensively considering the characteristics of these models, this study innovatively constructed an FINM prediction model to solve the trade-off problem between accuracy and efficiency in multi-feature time series prediction.  
Before constructing the FINM, we fully acknowledged that the existing models have limitations in dealing with complex time series data. For example, although the CNN is excellent in feature extraction, it struggles to fully capture long-term dependencies. Although the BiLSTM has a strong context modeling ability, it has low computational efficiency when dealing with long sequences. As a lightweight variant of the BiLSTM, the GRU alleviates computational pressure but has shortcomings in feature integration. In view of the fact that the neural network structure is critical to the model performance and learning algorithm, in this study, we selected the combined structures of CNNs, BiLSTMs, and GRUs. This is because a CNN can efficiently extract local features from sequences to provide rich information, a BiLSTM can mine long-term dependencies to improve prediction ability, and a GRU deeply refines and integrates input feature vectors with a simple structure and efficient computing performance. This combination not only fully harnesses the advantages of each model but also achieves the comprehensive modeling and accurate prediction of complex time series data by means of mutual synergy, so as to achieve a double improvement in prediction accuracy and computational efficiency.  
As shown in Figure 1, the input data are presented in a three-dimensional matrix form, covering dimensional information, such as the sample size, time step and feature number.  
Firstly, the data are normalized to construct an input matrix that meets the requirements of the FINM combination model, which provides a standardized data basis for subsequent model processing. The normalized data are imported into the one-dimensional convolutional neural network layer. In this process, the convolution kernel plays a role in extracting the local features of the sequence and then uses the maximum pooling operation to further reduce the difficulty and complexity of data processing, providing more representative and refined data features for subsequent model modules. The feature data output from the CNN layer is used as the input for the BiLSTM layer. With the powerful context modeling ability of the BiLSTM, the long-term dependencies in the sequence are deeply mined, and the feature expression of the data is further enriched to provide more comprehensive information support for accurate prediction. The feature vectors output by the BiLSTM layer are transmitted to the GRU layer. The update gate and reset gate inside the GRU work together to accurately control the transmission and forgetting process of information, fully refine and integrate the input feature vectors, and finally generate the prediction results. The output of the GRU layer is transmitted to the dense connection layer, and the sequence representation is further refined to extract higher-level and more abstract feature information. Finally, the processing result is transformed into the predicted passenger flow value through the activation function.