Abstract:
Abstract: Predicting agricultural product prices plays a key role in stabilizing the agricultural market. However， due to the influence of various factors， agricultural product prices exhibit characteristics such as non-linearity and periodicity， making it difficult to accurately predict. To solve this problem， a new agricultural product price prediction model， EMD-ConvNeXtV2-TimesNet， is proposed. The model introduces two innovations based on the TimesNet model: first， it innovatively incorporates an Empirical Mode Decomposition （EMD） module to decompose the original price series， thereby better capturing the intrinsic structural information of the price series； second， it improves the image feature extraction module of TimesNet to a ConvNeXtV2 Block to more effectively capture the cyclical information of prices. Comparative experiments were conducted on the collected datasets of corn， eggs， soybeans， and peanuts. The experimental results show that compared with the best prediction results of comparison models such as DLinear， Informer， Transformer， FiLM， FEDformer， the Mean Absolute Percentage Error （MAPE） and Mean Absolute Error （MAE） are reduced by 38.902%/38.562%， 33.183%/33.108%， 39.471%/35.178%， and 48.525%/47.806% respectively. The new model has achieved significant accuracy improvements. Ablation experiments further confirmed the complementary role of the EMD module and ConvNeXtV2 Block in the model， which more effectively reduces the price prediction error compared to the original TimesNet.

Key words: Key words: price prediction, agricultural products, TimesNet, EMD, ConvNeXtV2