An improved machine learning model Shapley value-based to forecast demand for aquatic product supply chain

Abstract

Previous machine learning models usually faced the problem of poor performance, especially for aquatic product supply chains. In this study, we proposed a coupling machine learning model Shapely value-based to predict the CCL demand of aquatic products (CCLD-AP). We first select the key impact indicators through the gray correlation degree and finally determine the indicator system. Secondly, gray prediction, principal component regression analysis prediction, and BP neural network models are constructed from the perspective of time series, linear regression and nonlinear, combined with three single forecasts, a combined forecasting model is constructed, the error analysis of all prediction model results shows that the combined prediction results are more accurate. Finally, the trend extrapolation method and time series are combined to predict the independent variable influencing factor value and the CCLD-AP from 2023 to 2027. Our study can provide a reference for the progress of CCLD-AP in ports and their hinterland cities.