## 3 Multi-Dimensional Fusion Feature-Enhanced Prediction Method
### Deep Fusion Module
#### 3.2.1 Convolutional Neural Network Structure  
This module uses a three-layer CNN structure. In this structure, the convolution layer, batch normalization layer, and pooling layer of each layer work together to gradually extract and optimize feature information. The initial convolutional block employs a one-dimensional convolution with 5 input channels and 64 output channels. The kernel size is set to 3, and the padding is 1. The formula for the convolution operation is as follows:  
\[output(n,c_{out},\ l)=bias(c_{out})+\sum_{c_{in}}^{c_{in}}weight(c_{out},\ c_{in})*input(n,\ c_{in},\ l) \tag{17}\]  
Here, \(n\) represents the number of samples, \(c_{in}\) and \(c_{out}\) represent the number of input and output channels, \(l\) represents the length of the sequence, and \(*\) represents the convolution operation. This configuration enables the convolutional layer to initially extract local feature patterns while preserving the original time series data, such as identifying short-term price fluctuations and the connections between neighboring data points. The formula for the batch normalization layer is as follows:  
\[\mu_{B}=\frac{1}{m}{\sum_{i=1}^{m}x_{i}}\,\ \sigma_{B}^{2}\frac{1}{m}{\sum_{i=1}^{m} \left(x_{i}-\mu_{B}\right)^{2}},\ \hat{x}_{i}=\frac{x_{i}-\mu_{B}}{\sqrt{\sigma_{B}^{2}+\varepsilon}},\ y_{i}= \gamma\hat{x}_{i}+\beta. \tag{18}\]  
Batch normalization is performed on the output of the convolutional layer, where \(m\) is the batch size, \(x_{i}\) is the input data, \(\mu_{B}\) and \(\sigma_{B}^{2}\) are the batch mean and variance, respectively, \(\varepsilon\) is a small constant, and \(\gamma\) and \(\beta\) are learnable parameters. The normalization accelerates  
Figure 3: Deep fusion module diagram (shows the architecture of CNN and XGBoost fusion, highlighting its synergistic advantages in time series feature extraction and nonlinear mapping). training and stabilizes the feature learning process to reduce the impact of changes in data distribution. The pooling layer executes a max pooling operation using a 2 \(\times\) 2 kernel. It captures the most prominent features within each local region, thereby reducing the data's dimensionality. This process effectively halves the length of the data sequence, creating a more condensed representation for subsequent feature extraction. In the second convolutional block, there are 64 input channels and 32 output channels, with a 3 \(\times\) 3 kernel size and padding of 1. This setup allows for the extraction of more sophisticated and abstract features from the data processed by the preceding layer. As the network deepens, it becomes capable of capturing feature dependencies over longer durations, including medium-term trend shifts in price movements and the interplay between various periodic fluctuations. A batch normalization layer is applied to this convolution's output to enhance the stability of the training process and boost learning efficiency. Additionally, a max pooling layer with a 2 \(\times\) 2 kernel is employed, which reduces the dimensionality of the data. Consequently, the length of the data sequence is halved, and the representation of data features becomes increasingly refined. In the third convolutional block, the input channels for the convolutional layer are 32, while the output channels amount to 16. The convolution kernel is set to a size of 3 with a padding of 1. This configuration continues to refine and integrate the features, extracting more representative and distinctive characteristics. Following the three-layer convolution process, the network can capture multi-scale feature information, ranging from local to global and from short-term to relatively long-term, providing a rich and effective feature input for the subsequent fully connected layer. The batch normalization layer normalizes the convolution output, ensuring stable model training. The pooling layer employs a max pooling approach with a kernel size of 2, halving the data sequence length.