Steel load prediction based on SSA-LSTM algorithm

In this paper, Pearson method was used to screen highly correlated factors and input them into the prediction model for training. Based on the LSTM model, this paper added the optimization of SSA search algorithm, and used the steel load data of a region in Gansu Province to verify the proposed model. Compared with the original model, R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> increased by 8.15% and MAPE decreased by 1.67%, indicating that the prediction effect of SSA-LSTM model was better.