Multidimensional Time Series Prediction Based on WOA-BP-A Case Study of Wordle

In recent years, the development of technology has led to an explosion of multi-platform heterogeneous data, and multi-dimensional time series prediction can bring rich benefits to enterprises. Traditional time series prediction models fail to capture the complex relationships between feature variables. To address this issue, this paper proposes a prediction model based on WOA-BP, which uses BP neural networks to fit the nonlinear relationships between multi-dimensional time series features and optimizes the BP neural network parameters through the WOA algorithm. The research results show that the model has good prediction performance (RMSE=2.7718) and can provide assistance for management decision-making.