## 1 Introduction  
With the globalization of the economy, the impacts of commodity price fluctuations have emerged as a significant concern in the context of sustainable socio-economic development. [PERSON] [1] proposed that when commodity prices rise too quickly, potential risks may arise in financial markets. Moreover, cost-driven inflation can occur, affecting the profits of small and medium-sized Enterprises in the midstream and downstream, which are intermediate links of the supply chain. [PERSON] et al. [2] have reported that the accurate prediction of price fluctuations is beneficial with regard to risk management and helps in the pricing of downstream products. Meanwhile, the price fluctuation of agricultural products is related to the income of farmers and the living standard of residents and also has an impact on the stability of the social economy. Regarding the actual transaction of bulk commodities, there exist small samples with short collection periods or sparse data recorded with low trading frequency. The use of such data for model training can lead to overfitting and makes it difficult to determine fine-grained changes in the process of price forecasting. Data augmentation is beneficial to monitor price fluctuations and provide insights for intelligent decision-making. Data augmentation is often used in image processing, such as translation, rotation, scaling, and other affine transformations. As for time-series data, geometric transformations are often adopted, as well as window cropping or the addition of noise in the time and frequency domains. There still exist shortcomings regarding the data analysis of the overall distribution characteristics [3]. Generative Adversarial Networks (GANs) can be utilized to generate data, such as T-CGAN, RCGAN, and so on. However, these methods face challenges related to parallelization, and it is necessary to further validate their generalization ability. Forecasting algorithms commonly include statistical, machine learning (ML), deep learning (DL), and Integrated Learning methods, among others. Traditionally, statistical methods are based on linear structures and may not perform well on complex, multi-feature, and non-linear data. Machine learning approaches offer flexibility in feature correlation analysis, overfitting, and regularization techniques but face performance and accuracy bottlenecks when dealing with long time series. Deep learning approaches have the potential to reduce human involvement in feature engineering and enable the rapid perception of market changes. Small sample-based prediction poses difficulties related to capturing the intrinsic complex relationships among features and the regularity of distributions when using traditional algorithms. Taking into account the above, the purpose of this research is not only to apply TimeGAN to augment the obtained data in order to prevent overfitting on sparse or small samples but also to reduce the runtime and computing costs. For this purpose, a multi-scale comparison method is proposed. The key contributions of this study are summarized as follows: (1) In the field of commodities, traditional machine learning methods often struggle with low prediction accuracy when using sparse and small sample data sets, whereas deep learning techniques are prone to overfitting. Our study mainly focuses on utilizing TimeGAN to augment the data and improving the training performance of the models. (2) A multi-scale forecasting strategy is employed, which combines XGBoost with TCN-attention to predict the short-term price of agricultural commodities at daily and hourly levels. The proposed method is compared with the GRU, LSTM, and TCN algorithms in terms of computational cost, time, and accuracy. Additionally, the Transformer and TimesNet methods are also considered for comparison. The experiment involves data augmentation of hourly and daily data for electronic trading or spot transactions. Moreover, the study compares the outcomes of price and volatility predictions obtained with different methods.