## 5 Discussion  
During the development of our research, it was observed that seasonal commodities typically have a small sample size and long data collection intervals, prompting us to propose a strategy to address this issue. TimeGAN has been widely used in engineering fields, but is less common in the domain of bulk commodities. Our experiments revealed that the accuracy of forecasts for periods extending beyond 10 days or 10 h is relatively low. However, predictions for 5 days and 5 h tended to converge after approximately 30 iterations, demonstrating practical value. During the experiments, a low-cost GPU was used, and the model's stability was validated through the ablation experiments detailed in Section 4.3.3. As the model was applied to real-time data, only its usability was verified in this study. In future research, more influencing factors--such as weather, exchange \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variety** & **Method** & **MAE** & **RMSE** & **MAPE** & \(\mathbf{R^{2}}\) & **Time(s)** \\ \hline \multirow{4}{*}{Garlic} & XGBoost+TCN-Att & 0.002 & 0.002 & 0.006 & 0.940 & 186.196 \\  & Timegan+TCN-Att & 0.008 & 0.01 & 0.146 & 0.986 & 5085.855 \\  & TCN-Att & 0.005 & 0.006 & 0.016 & 0.546 & 202.499 \\  & Xgboost+TimeGAN-TCN-Attention & 0.006 & 0.008 & 0.104 & 0.991 & 5410.65 \\ \hline \multirow{4}{*}{Ginger} & XGBoost+TCN-Att & 0.017 & 0.021 & 0.029 & 0.863 & 215.03 \\  & Timegan+TCN-Att & 0.033 & 0.067 & 0.088 & 0.884 & 5138.506 \\  & TCN-Att & 0.028 & 0.037 & 0.046 & 0.540 & 205.682 \\  & Xgboost+TimeGAN-TCN-Attention & 0.035 & 0.053 & 0.072 & 0.930 & 5264.069 \\ \hline \multirow{4}{*}{Chili} & XGBoost+TCN-Att & 0.007 & 0.013 & 0.072 & 0.100 & 125.56 \\  & Timegan+TCN-Att & 0.067 & 0.107 & 0.223 & 0.922 & 3093.66 \\ \cline{1-1}  & TCN-Att & 0.004 & 0.005 & 0.048 & 0.749 & 111.655 \\ \cline{1-1}  & Xgboost+TimeGAN-TCN-Attention & 0.061 & 0.092 & 0.197 & 0.942 & 3155.873 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation experiments. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Days**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Garlic**} & \multicolumn{3}{c}{**Ginger**} & \multicolumn{3}{c}{**Chili**} \\ \cline{3-10}  & & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** & **MAE** & **RMSE** & **MAPE** \\ \hline \multirow{4}{*}{5} & GRU & 0.026 & 0.03 & 0.03 & 0.04 & 0.04 & 0.065 & 0.013 & 0.013 & 0.176 \\  & LSTM & 0.025 & 0.028 & 0.028 & 0.026 & 0.027 & 0.042 & 0.019 & 0.022 & 0.267 \\  & TCN & 0.014 & 0.016 & 0.016 & 0.036 & 0.038 & 0.058 & 0.022 & 0.025 & 0.297 \\  & TCN-Attention & 0.049 & 0.051 & 0.056 & 0.037 & 0.037 & 0.06 & 0.004 & 0.005 & 0.06 \\ \hline \multirow{4}{*}{10} & GRU & 0.023 & 0.028 & 0.027 & 0.039 & 0.039 & 0.063 & 0.011 & 0.012 & 0.152 \\  & LSTM & 0.029 & 0.034 & 0.032 & 0.027 & 0.028 & 0.043 & 0.022 & 0.025 & 0.287 \\ \cline{1-1}  & TCN & 0.028 & 0.04 & 0.033 & 0.039 & 0.041 & 0.064 & 0.035 & 0.039 & 0.447 \\ \cline{1-1}  & TCN-Attention & 0.045 & 0.048 & 0.05 & 0.04 & 0.000 & 0.064 & 0.011 & 0.014 & 0.139 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Forecasting at the daily level. rates, and regional differences--should be incorporated for further validation. Based on the experimental results, we discuss various issues below. On one hand, the error evaluation indicated that the proposed method may provide improved accuracy when compared to various existing methods. It performed better than machine learning methods such as SVR or RF. Furthermore, TCN-attention and TCN cost less time than GRU and LSTM, but the running time is also related to parameter settings. The traditional Transformer model presented a negative \(R^{2}\) and, so, does not accurately capture the relationships between sequences. The accuracy of TimesNet was similar to that of the proposed method, and its accuracy is closely related to its parameter adjustment.