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hungchun-lin / Stock-price-prediction-using-GAN

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.
MIT License
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Training error #1

Closed sujanme25 closed 3 years ago

sujanme25 commented 4 years ago

Getting error in training. with 8 dimension and 17 features.

:34 train_step * real_y_reshape = tf.reshape(real_y, [real_y.shape[0], real_y.shape[1],1]) C:\Users\sujan\anaconda3\lib\site-packages\tensorflow\python\framework\tensor_shape.py:887 __getitem__ return self._dims[key].value IndexError: list index out of range
hungchun-lin commented 3 years ago

@sujanme25 You can retry the code now, it was in the developing process, so might not have some mistake.