Time-series Prediction Model of Stock Price Based on MVO-CNN-BiLSTM
In order to solve the difficult problem of predicting the trend of stock index time series,a hybrid model(MVO-CNN-BiLSTM)for stock prediction is proposed,which introduces MVO,and combines BiLSTM and CNN.The significance of the model is that the MVO has the characteristics of global search ability and fast convergence speed,which is suitable for optimization problems.The CNN can effectively extract features from the data in the first few layers,and BiLSTM can model the time-series relationship between these features in subsequent layers.Finally,the simulation results of 5 378 sets of data from 2002 to 2024 of CSI 300 index show that the prediction effect of this method is obviously better than that of traditional CNN-LSTM network model and CNN-BiLSTM network model after determining the optimal time step,which can effectively reduce the prediction error.
Bidirectional Long Short-Term Memory networkMulti-Verse OptimizationConvolutional Neural Network
万方数据
维普
