An Aquatic Product Price Forecast Model Using VMD-IBES-LSTM Hybrid Approach

Changes in the consumption price of aquatic products will affect demand and fishermen’s income. The accurate prediction consumer index provides important information regarding product market. Based on non-linear non-smooth characteristics fishery series, this paper innovatively proposes a forecasting model that is based Variational Modal Decomposition Improved bald eagle search algorithm optimized Long Short Term Memory Network (VMD-IBES-LSTM). Empirical analysis was conducted using fish data from Department Marketing Informatization Ministry Agriculture Rural Affairs China. proposed study subsequently compared with common models such as VMD-LSTM SSA-LSTM. research results show VMD-IBES-LSTM constructed has good fitting high accuracy, which can better explain seasonality trends change China’s index, provide scientific effective method for relevant management departments units to predict price, have certain reference value reasonably coping fluctuation market price.