In this project, we will compare two algorithms for stock prediction. First, we will utilize the Long Short Term Memory(LSTM) network to do the Stock Market Prediction. LSTM is a powerful method that is capable of learning order dependence in sequence prediction problems. Furthermore, we will utilize Generative Adversarial Network(GAN) to make the prediction. LSTM will be used as a generator, and CNN as a discriminator. In addition, Natural Language Processing(NLP) will also be used in this project to analyze the influence of News on stock prices.
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Basic GAN test data plot / missing plot functions #9
Hello,
would you please add the "Basic GAN test data plot" (Fig. 7 in jcssp.2021.188.196.pdf) missing plot functions?
I.e.:
def plot_testdataset_with2020_result(X_test, y_test):
....
test_with2020_RMSE = plot_testdataset_with2020_result(X_test, y_test)
print("----- Test_RMSE_LSTM_with2020 -----", test_with2020_RMSE)
Thank you for your time,
LM
Hello, would you please add the "Basic GAN test data plot" (Fig. 7 in jcssp.2021.188.196.pdf) missing plot functions? I.e.:
def plot_testdataset_with2020_result(X_test, y_test): .... test_with2020_RMSE = plot_testdataset_with2020_result(X_test, y_test) print("----- Test_RMSE_LSTM_with2020 -----", test_with2020_RMSE) Thank you for your time, LM
jcssp.2021.188.196.pdf