Price Forecasting of Aquatic Products Based on Weight Allocation Intelligent Combinatorial Modelling

Abstract

Abstract The price prediction of aquatic products is of great significance to the socio-economic development and fisheries industry. However, due to the complexity and uncertainty of the aquatic product market, traditional forecasting methods often struggle to accurately predict price fluctuations. Therefore, this study adopts a intelligence combination model to enhance the accuracy of aquatic product price prediction. Firstly, three decomposition methods, namely empirical wavelet transform, singular spectrum analysis, and variational mode decomposition, are applied to decompose the complex original price series. Secondly, a combination of bidirectional long short-term memory artificial neural network, extreme learning machine, and exponential smoothing prediction methods is used for cross-prediction on the decomposed results. Subsequently, these predicted result are input into the PSO-CS intelligence algorithm for weight allocation and generating combined prediction results. Empirical analysis is conducted using the data of daily sea purchase price of larimichthys crocea in Ningde City. The combination prediction accuracy with PSO-CS weight allocation is found to be higher than that of single model predictions, yielding superior results. Based on the weight allocation intelligent combinatorial modelling, the prediction of aquatic product prices demonstrates higher accuracy and stability, enabling better adaptation to market changes and price fluctuations.