LSTM-Neural-Network-for-Time-Series-Prediction

All these LSTM Stock Market tutorials seem to be a variation of this guys tutorial which he did a while ago: https://github.com/jaungiers/LSTM-Neural-Network-for-Time-Se... Except Jakob explains the folly of the method. Whereas the author of this piece says the following: From the plot we can see that the real stock price went up while our model also predicted that the price of the stock will go up. This clearly shows how powerful LSTMs are for analyzing time series and sequential data.

I see here that original poster (OP) of the post tried to use many-to-one LSTMs instead of many-to-many LSTMs. I tell that first by looking at the charts. Then I saw the method named "predict_point_by_point" with the comment "Predict each timestep given the last sequence of true data, in effect only predicting 1 step ahead each time" in his code here: https://github.com/jaungiers/LSTM-Neural-Network-for-Time-Se... I strongly think the system would be better to perform many predictions at once instead, using seq2seq neural networks. The problem is properly explained here at the beginning of this other post: https://github.com/LukeTonin/keras-seq-2-seq-signal-predicti... This other post is, in turn, derived from my original project here doing seq2seq predictions with TensorFlow: https://github.com/guillaume-chevalier/seq2seq-signal-predic..