Details

Autor(en) / Beteiligte

• Sun, Yu
• Mutalib, Sofianita
• Omar, Nasiroh
• Huang, Mingjing

Titel

Research on the Application of LSTM-SA-AdaBoost Hybrid Model in Stock Forecasting

Ist Teil von

• 2023 5th International Conference on Applied Machine Learning (ICAML), 2023, p.310-315

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Quelle

IEEE Explore

Beschreibungen/Notizen

• To improve the fitting and accuracy of stock prediction, an improved deep neural network combined with AdaBoost model (LSTM-SA-AdaBoost) is proposed. The model feature engineering includes data cleaning, correlation analysis and normalization. The model uses simulated annealing algorithm to optimize the model parameters. The attributes after feature selection will be trained, and the predicted attributes will be predicted and optimized iteratively through the two-layer LSTM network. From the experiment results, it shows that LSTM-SA-AdaBoost algorithm is superior to the unmodified LSTM-AdaBoost model and LSTM-XGBoost model, compared with the single-target feature selection algorithm of LSTM and RNN network models, it has better fitting and better accuracy.