## 5 Discussions  
The MDFFE model proposed in this study achieved excellent performance in the crude oil futures price prediction task, which is manifested in extremely low prediction error (MAE = 0.0068) and an R-Squared value of 0.9970. These results significantly exceed a variety of benchmark models, including BiLSTM, CNN, LSTM, and Transformer variants. We understand that such a high R-Squared value may cause concerns about overfitting in the field of time series prediction, as well as questions about information leakage. In this regard, we ensure the robustness and generalization ability of the model through a number of strategies: First, the model is fully trained on massive historical data, aiming to learn universal laws rather than noise; secondly, the data are strictly and randomly divided into training and test sets, and the consistency and robustness of the performance of the model on different data subsets are verified by experiments with multiple rounds of random seeds. This method effectively simulates the effect of cross-validation. Finally, we strictly eliminate the leakage of any future information in the whole data processing process and ensure the authenticity of the prediction. The excellent performance of the MDFFE model is mainly attributed to its innovative methodology: it comprehensively integrates market dynamic information through fine multi-dimensional feature engineering, especially in that the model can effectively use the feature relationship between crude oil and hot-rolled coil futures data sets to enhance the prediction of crude oil prices. More importantly, it skillfully combines the local pattern capture ability of CNN with the nonlinear modeling and integrated learning advantages of XGBoost. This hybrid architecture forms a strong complementary effect, which significantly improves the prediction accuracy and stability. Although this study mainly demonstrates the excellent performance of the MDFFE model in crude oil futures price forecasting, we believe that its core design concept and methodology have good universality and scalability, and are expected to show similar excellent performance in other types of futures or asset price forecasting. The reasons are as follows:  
The versatility of multi-source data fusion: Our model design can effectively fuse data from different assets to enrich the feature set. Even if the prediction target is a single asset, it can also capture more complex market dynamics by introducing information on related assets. This method can be easily extended to other financial products. For example, when predicting a stock, data such as its industry index and related company's stock can be included at the same time. Universality of feature engineering: The multi-dimensional feature engineering methods used in the model (e.g., time series features, technical indicators, and cross-market correlation analysis) have universal predictive value in various financial markets. Whether it is stocks, foreign exchange, bonds, or other commodity futures, their price movements are often affected by similar time, technology, and interrelated factors. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **sum\_sq** & **df** & **F** & **PR (>F)** \\ \hline C (model) & 671.944528 & 8.0 & 7053.753944 & 0.0 \\ Residual & 2304.543389 & 193,536.0 & NaN & NaN \\ \hline \hline \end{tabular}
\end{table} 
Table 6: ANOVA analysis of variance results. Advantages of the hybrid model: CNN performs well in capturing local features and short-term patterns in a time series, while XGBoost, as a powerful ensemble learning model, is good at dealing with complex nonlinear relationships and high-dimensional data. This combination can effectively extract and learn the deep rules of price changes from different levels. The advantages of this hybrid structure are not limited to the crude oil market.