基于改进TimesNet模型的农产品价格预测方法OA

Predicting agricultural product prices plays a key role in stabilizing the agricultural market.However,due to the influ-ence of various factors,agricultural product prices exhibit characteristics such as non-linearity and periodicity,making it diffi-cult to accurately predict.To solve this problem,a new agricultural product price prediction model,EMD-ConvNeXtV2-Ti-mesNet,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 da-tasets of corn,eggs,soybeans,and peanuts.The experimental results show that compared with the best prediction results of com-parison 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 pre-diction error compared to the original TimesNet.