## 6 Conclusions  
This study proposed a cost-effective method with low run-time for predictive analysis using deep learning models on small sample data. Furthermore, the generalization ability of the model in a practical application considering bulk agricultural commodities was assessed based on previous research outcomes from different perspectives.  
A forecasting method combining TimeGAN with TCN-attention was presented for small sample data of agricultural commodities. The proposed approach tackles the issues of overfitting and gradient disappearance, enabling prediction on small sample data. TimeGAN is utilized to augment the obtained data, with similarity evaluation of the synthetic data conducted using t-SNE. Meanwhile, XGBoost is exploited to extract important features. Subsequently, price forecasting is performed with TCN-attention. The results of experiments comparing the proposed model with the TCN, GRU, and LSTM models demonstrated that the proposed method can increase the data volume for small samples, mitigate overfitting, and enhance the prediction accuracy.  
When dealing with small-sample data, the proposed method improves the model's training performance. However, for different varieties, accuracy improvement requires parameter adjustment, and it is essential to further enhance the generalization ability of the model with respect to different varieties. In practical applications, method selection must also take deployment costs into account, as newer methods with deep network layers often pose higher demands regarding the hardware configuration. Additionally, attention should be paid to the interpretability of prediction models in order to improve the reliability of price forecasting in real-world scenarios. The presented method is not limited to agricultural products. Additionally, the method combining XGBoost with TCN-Attention was utilized in our previous publications on power load forecasting, demonstrating the method's generalization performance. However, the proposed method faces limitations in terms of volatility forecasting, due to the minimal fluctuations in the normalized data. Optimizing the data augmentation algorithm is crucial for enhancing the effectiveness of model training in improving its volatility prediction performance, and further research is needed to tackle the issue of sparse data forecasting.  
Methodology, T.Y. and Y.L.; validation, T.Y. and Y.L.; data curation, T.Y. and Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L.; visualization, T.Y.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
The work was supported in part by the Science and Technology Planning Project of Beijing Municipal Education Commission (No. KM202011232022).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors wish to thank [PERSON] for valuable advice and [PERSON] for assistance with the experiments.  
**Conflicts of Interest:** The authors declare no conflicts of interest.