Grain Price Prediction Model Based on TimeF-iForest-Transformer

Grain price has a significant impact on social stable and economy development. However, the price of grain are often influenced by various complex factors, particularly the time factors. Therefore, this paper first splits the time characteristics into more fine-grained time characteristics of multiple dimensions to capture the seasonal changes, holiday effects, long-term trends, and short-term fluctuations in prices. Secondly, the isolated forest algorithm is employed to remove outliers from the dataset, ensuring the accuracy and reliability of the data set. Finally, adjust the Transformer model structure. This study removes the positional embedding layer and directly utilizes the encoding part of the Transformer model for feature extraction and representation of the input sequence, while defining the prediction layer to replace the decoder layer for numerical prediction. This design simplifies the model structure, reduces computational complexity and the number of parameters, and aligns better with the goals and requirements of regression prediction tasks. The experimental results demonstrate that the time feature decomposition method and the isolation forest algorithm significantly improve the accuracy of price prediction. Furthermore, the modified Transformer model in this study outperforms other machine learning methods and neural network algorithms in multiple evaluation metrics.