A Stock Index Prediction Model Based on LSTM: Taking New Energy Vehicles as an Example
DOI:
https://doi.org/10.54097/gmm2kih5Keywords:
Singular Spectrum Analysis; Long Short-Term Memory; Stock Index Prediction.Abstract
Stock indices can partially reflect the overall price level and variations of various stocks in the market. Therefore, analyzing the stock index of new energy vehicles aids in predicting future market trends. However, stock prices, as time series data, display traits of nonlinearity and high noise. This study proposes employing the denoising method of Singular Spectrum Analysis (SSA) on the CNI New Energy Vehicles Index (399417) and constructing an SSA-LSTM model. Comparative results with a single LSTM model demonstrate that the evaluation indicators R2 of the SSA-LSTM model increase by 0.06, RMSE decreases by 54.37, and MAE decreases by 49.49. These findings suggest that SSA can enhance the predictive accuracy of the model.
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