Amazon Stock Forecasting in PyTorch with LSTM Neural Network (Time Series Forecasting) | Tutorial 3

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Great content here Greg! I had so much to learn from this video, specifically as I coded it along with your video. I also happened to play around with the model architecture and the inputs in terms of trying out a bidirectional LSTM, GRU, increasing sequence length, and by extending the input features by incorporating other columns. Thank you again!

What timing, Greg! You just published a video I was looking for. Thanks a lot!

Keep it up Greg! Enjoying this series very much 😊

Thank you so much I was really struggling figuring out how to format the data to feed into an lstm model in pytorch, this really helped conceptualize it.

Thank you very much! you are a life saver!!

Thanks a lot Greg, all your videos on LSTMs are really helping with my Master-Thesis!

It's not a prediction, it is simple lag.

Thank you very much for the clear instructions! Thanks to you, I launched my first neural network! Greetings from Russia :)

Another amazing guide 👌👏🙏

You have an irregularly sampled time series (so under this approach t-7 for one row may actually be 9 days prior). I realize opening that can of worms gets into a whole niche-y area rife with salami slicing publications. But would've been really great to see it addressed with a carry forward or something.

Hi Greg, is it ok scaling the entire data? Bcz most of the time we do scale only the train set

thank you Greg! I'm curious if you include more parameters in X( training datasets), for example 5 parameters instead of 1 parameter, but also look back 7 days, how to reshape your data input (X_Train) structure? thanks!

If you only scale the X data and not the y data, the predictions will be in normal scale and there is no need to perform inverse transform on y_pred. 😀

Hello Greg, nice video its really helping to understand deeply LSTM and PyTorch, I have a one question. If we need to add more than one features to predict what we need to do on lookback ?

Very interesting!

Hi Greg, great content! Just wanted to say that the win-rate is more useful to test if the model is any good, you can calculate the winrate by simply counting how many times the predicted direction (up/down) is correct

Why do we need to do the min-max scaling to the data if there is only one feature? Also, why is it necessary that we create a custom dataclass? Can you elaborate on that?

Can you explain why every content on Tensor Flow is shitting image recognition? And if we want to build visualization , data pipeline, real time cluster and decision making ? Shall I go with Pytorch? I guess tensor flow either don't has utility for numbers?

Hello, great tutorrial! Since you made a comment on the inversion: To avoid the workaround with the dummies on the inversion, you will need to create 2 different scalers, one for X and one for y. Then you can separately inverse the scale without the need to create the dummy value matrix. Something like this: mm_scaler_x = MinMaxScaler(feature_range=(-1, 1)) mm_scaler_y = MinMaxScaler(feature_range=(-1, 1)) orig_dataset = create_feature_set_df(ds.df['Close'].to_frame(), LOOBACK_STEPS).to_numpy(dtype=np.float32) X_orig = orig_dataset[:, 1:].copy() X_orig = np.flip(X_orig, axis=1) y_orig = orig_dataset[:, 0].copy().reshape(-1, 1) X = mm_scaler_x.fit_transform(X_orig) y = mm_scaler_y.fit_transform(y_orig) Also, since the dataframes/numpy arrays do not contain objects, it is sufficient to use the numpy or dataframe copy functions (no need for deepcopy). Thank you for your great videos! I learn so much from them!