Open AccessCSI 300 Prediction Using LSTM Model
Author(s) -
Jiaming Mai
Publication year - 2021
Publication title -
bcp business and management
Language(s) - English
Resource type - Journals
ISSN - 2692-6156
DOI - 10.54691/bcpbm.v13i.102
Subject(s) - computer science , artificial intelligence , volume (thermodynamics) , test (biology) , machine learning , econometrics , natural language processing , data mining , statistics , mathematics , paleontology , physics , quantum mechanics , biology
LSTM is used in the article to forecast CSI 300, and consider if adding volume (the number of shares transacted every day) and p_change (amount of increase and amount of decrease) on the basis test (the common variables include open, close, high and low price) will have a better result; The result is also compared with predictions using SVR and the GBDT model, and the MSE of the LSTM and SVR test are lower than GBDT model.