Two‐Stream Reinforcement Ensemble Framework for Agricultural Commodity Prices Forecasting Using Textual Data

Influenced by various complex factors, the price series of agricultural futures exhibit nonstationarity. Existing research often presumes that the relationship between inputs and outputs remains stable throughout the training process. This assumption makes it challenging to dynamically adjust the weights of various models based on data characteristics. Furthermore, existing studies focus only on modeling variable dependencies, overlooking the impact of variable independence on model robustness. Therefore, this paper proposes a two‐stream ensemble forecasting model that integrates a dynamic sentiment index. Initially, ChineseBERT and textCNN are employed to classify the sentiment of news texts, calculating the sentiment scores. Subsequently, weight factors are designed based on daily price fluctuations to adjust these sentiment scores, ensuring they accurately reflect the impact of news sentiment on market prices. In the model construction phase, multivariate time series data are input into two distinct models: one model is dedicated to capturing temporal dependencies, while the other focuses on capturing intervariable dependencies, thereby providing diverse yet complementary predictive insights. An online convex optimization approach is then utilized to learn the optimal combination weights. During the testing phase, reinforcement learning is applied to dynamically adjust the prediction weights of these two models. The effectiveness of the proposed methods is validated using soybean and corn futures prices. Experimental results demonstrate that the proposed two‐stage sentiment index (TPSI) exhibits strong predictive capability for agricultural futures prices, achieving high accuracy in short‐term and medium‐term price forecasts.