## 2 Related Work  
In the domain of predicting crude oil and hot-rolled coil futures prices, numerous researchers conducted comprehensive and detailed studies, yielding various outcomes. However, they still encounter several hurdles and constraints. Early research focused on traditional statistical analysis methods [9]. For example, the linear regression model [10] is widely used to explore the linear relationship between prices and a few macroeconomic variables, trying to predict futures prices by establishing simple linear equations. However, this method oversimplifies the complexity of the market and cannot capture the nonlinear factors behind price changes. The auto-regressive moving average model [11] is designed to forecast future values by leveraging the past data from the price series. Although the characteristics of the time series are considered to a certain extent, it is less adaptable to sudden external shocks and multi-factor interactions in the market. With the deepening of financial market research, some machine learning methods have been gradually introduced. A support vector machine [18] has certain advantages in dealing with small sample and nonlinear problems and has been used in futures price forecasting. For example, [PERSON] et al. [19] combined fuzzy information granulation with support vector machine to show good results in crude oil futures price forecasting, which effectively improved the prediction accuracy. [PERSON] et al. [20] used the fusion of Kalman filter and support vector machine to improve the effectiveness of the stock index future investment strategy. [PERSON] et al. [21] explored the application of a support vector machine in financial time series prediction earlier, which provided a theoretical basis for subsequent research. [PERSON] et al. [22] optimized the parameters of the support vector machine and improved the accuracy of judging the direction of the stock market. The random forest algorithm [23] predicts by constructing multiple decision trees [24], which can deal with high-dimensional data and have good anti-noise ability. However, it performs poorly in dealing with the long-term dependencies of time series data. [PERSON] et al. [25] integrated a multi-class classifier and random forest, and the constructed system showed good practicability and effectiveness in financial transaction scenarios. [PERSON] [26] used the random forest regression model to predict the price of treasury bond futures, effectively dealing with high-dimensional data and nonlinear relationships. In recent years, deep learning methods have begun to emerge in futures price forecasting. Recurrent neural networks (RNN) [27] and their variants of long-short term memory (LSTM) [28] and gated recurrent units (GRU) [29] have been used to deal with the dependency problem of time series data. They can learn the long-term pattern in the price series to a certain extent, but in the face of large-scale and multi-dimensional data, the complexity and computational cost of model training increase sharply, and they are prone to the problem of gradient disappearance or gradient explosion. For example, [PERSON] et al. [30] used the LSTM network to construct a futures price prediction model, which provides a new idea and reference for investors to grasp the futures price trend. [PERSON] et al. [31]integrated multiple factors to predict the futures price of corn and committed to improving the accuracy of prediction. [PERSON] et al. [32] combined a variational auto-encoder (VAE) with an attention mechanism-improved gated recurrent unit (AMGRU) to construct a more targeted stock index futures price prediction model. In the feature engineering of financial market forecasting, previous studies often focused on a single type of feature. For example, the research of [PERSON] et al. [33] and [PERSON] et al.