## 3 Principles of Price Forecasting Combined with GAN  
This study focuses on utilizing TimeGAN to generate data for small samples of agricultural products. Meanwhile, similarity evaluation is used to assess the difference between the generated data and the original data. Furthermore, the XGBoost method is utilized to extract important features, followed by price prediction using a TCN-attention network. The price forecasting model is composed of an input layer, data generation, feature extraction, TCN Residual Blocks, an attention mechanism, and an output layer, as shown in Figure 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Method** & **Description** \\ \hline Statistical methods & AR, MA, ARMA, ARMA, ARMA, ARCH, GARCH, and so on. & Traditional statistical methods relying on time-series prediction from a data distribution. These methods often make assumptions about the data and face limitations in analyzing complex non-linear data sets. \\ \hline Machine learning & XGBoOST, SVR, EEMD, and so on. & Machine learning approaches offer greater flexibility for correlation analysis, overfitting control, and regularization techniques. However, bottlenecks still exist when processing large data sets. Machine learning methods show improved performance, but still encounter issues with large-scale data. \\ \hline Deep learning methods & RNN, LSTM, GRU, TCN, GNN & RNN: Difficulties in handling gradient disappearance, long-term dependencies, and parallelization. GRU: Faster convergence due to fewer parameters, but still prone to gradient vanishing and exploding. LSTM: Handles long-term dependencies better, but is computationally expensive. GNN: Effective for spatio-temporal modeling, although traditional GNNs struggle with dynamic time-series and high computational complexity. \\ \hline Transformer methods & Transformer, Autoformer, Informer, and so on. & Transformers applied to long-term time-series forecasting face quadratic time complexity and high memory consumption. The use of Informer reduces these issues, enabling the handling of long sequences, but Transformer algorithms still have high deployment costs. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The key characteristics of various methods. Step 1: Multi-dimensional data of agricultural products (e.g., garlic or ginger) are used as input. Data pre-processing mainly deals with the original data using a sliding window method and includes excluding outliers, filling missing values, and other processes. Step 2: New data are generated using the TimeGAN framework [33], and a dual-scale measurement approach is used to evaluate the quality of the generated data. To measure the proximity between the generated and original data, the t-distributed Stochastic Neighbor Embedding (t-SNE) method and a visualization approach are utilized. Step 3: Correlation analysis among features is carried out, exploiting XGBoost to extract important features. Step 4: TCN residual blocks and dilated convolution are leveraged to reduce the processing time for long sequences. Meanwhile, an attention mechanism is used to improve the forecasting accuracy. Step 5: Three groups of agricultural product data are used for experimentation in order to compare the predictive outcomes of the proposed model at multiple scales with those obtained using LSTM, GRU, TCN, Transformer, and TimesNet models. A comparative analysis is conducted by evaluating metrics such as R-squared (\(R^{2}\)), Root Mean Squared Error (RMSE), and so on. During model training, small samples can lead to the overfitting of deep learning models, while they may result in lower prediction accuracy of machine learning methods. TimeGAN can capture correlations between time series to generate highly similar data. Therefore, it is utilized in this study to increase the data volume, with the aim of improving model performance. Important features are extracted using XGBoost, which helps to reduce the computational complexity.