EMD-TCN-TimeGAN: A Data Augmentation Model for Enhancing the Accuracy of Agricultural Product Price Prediction

Accurately predicting agricultural product prices is crucial for the stability of the agricultural market. However, due to the collection intervals of agricultural price data, which are typically daily, weekly, or monthly, the data volume is relatively small. This limitation affects the accuracy of deep learning models in price prediction. Data augmentation is an effective method to improve the accuracy of agricultural price predictions. This research proposes a novel data augmentation model, EMD-TCN-TimeGAN. Based on TimeGAN, this model has been significantly improved by replacing the RNN-based components with TCN, which enhances the model’s ability to capture long-term dependencies. Additionally, the model incorporates an EMD module to decompose the original price series, thereby better capturing the intrinsic structural information of the time series and further improving the quality of the generated data. Experiments conducted on a collected corn dataset show that adding the augmented data to the training set of the prediction model and retraining it significantly improves the prediction accuracy. The coefficient of determination (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) increased from 0.928831 to 0.968742, and the mean absolute error (MAE) decreased from 0.026023 to 0.016628. Comparisons with the TimeGAN model using RNN components validate the effectiveness of replacing the components with TCN; comparisons with TCN-TimeGAN validate the effectiveness of the EMD module in further improving prediction performance. The EMD-TCN-TimeGAN proposed in this study generates sequences that better preserve the temporal dynamics of the time series data, maintaining the temporal relationships between the original variables. This improves the accuracy of small-sample agricultural price predictions, which is of significant importance and benefit to agricultural production and related activities.