Analyzing the Construction of Prediction Models Based on LSTM Machine Learning

A time series prediction model based on LSTM is constructed, and a gating mechanism is introduced to effectively model the nonlinear and long-term dependent series features. On the basis of traditional models such as ARIMA and SVR, the network structure is optimized by combining the deep learning framework, and the sliding window input and multi-parameter tuning strategy are designed. The experimental results show that the model outperforms the comparison model in MSE, MAE and RMSE, in which the MSE is as low as 10.92, and the standard deviation of the error is controlled within 1.05, which possesses strong prediction accuracy and stability, and provides a reference path for the modeling of complex sequences.