CSI 300 Prediction Using LSTM Model | Zendy

Jiaming Mai | Zendy

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CSI 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.

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