Article Details

Abstract

As one of the important economic crops in China, the price fluctuation of green onions has a significant impact on the agricultural economy, government decision-making, and consumers' daily lives. However, due to the combined effects of various complex factors in the agricultural product market, such as climatic conditions, market supply and demand changes, and policy adjustments, the price of green onions exhibits significant nonlinear and non-stationary characteristics, posing great challenges to price prediction. To address this issue, a prediction framework combining the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Long Short-Term Memory (LSTM), and attention mechanism is proposed to improve prediction accuracy and solve the modeling problem of complex time series. This not only provides a new approach for green onion price prediction but also accumulates practical experience in the field of agricultural product price prediction.

Keywords

green onion price forecasting; long short-term memory network; attention mechanism; time series analysis

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About the authors

Liu Yong (July 1985-), male, Han ethnicity, from Dezhou, Shandong Province, associate professor, senior engineer, master's student, research direction: artificial intelligence, agricultural engineering and information technology.