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

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[alexanderskusnov]

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

[googm]

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.

[arbaazmohammed]

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!

[robinkhn]

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

[evolv_]

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. 😀

[ArthurRabatin]

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(),

X_orig = orig_dataset[:, 1:].copy()
X_orig = np.flip(X_orig, axis=1)
y_orig = orig_dataset[:, 0].copy().reshape(-1, 1)

X =
y =

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!

[dong]

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

[grand]

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

[karlbooklover]

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

[Sccoropio]

how can i use this to predict next week's prices?

[pfinbeijing]

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!

[otaviocoutinho]

Im getting a little confused in how would you apply te model to actually predict days ahead in the future, since in this LSTM the future days are not in the dataframe. I imagine a non trivial implementation so the model takes always the last days available.
Could anyone give a hand with that?

[whichdude]

Keep it up Greg! Enjoying this series very much 😊

[waitingonacheck]

Y’all know that this is just an overblown arima model.. with no predictors other than error terms in the series. Where did you evaluate model performance on out of time data? What’s the conclusion here? What lag was best? This isn’t predictive it’s explanatory analysis

[elliotcossins]

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.

[ktmytgq]

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

[abiagarwal]

so how does the graph work?
how do I test the data for future? I don't have the actual future data, this makes sense fir backtesting, but what about for forecasting?

[ljruscf]

Thank you for this tutorial. However, I was wondering whether there was a possibility of data leaking from training to testing given that you scaled all the data and then split it.

[MrCelal]

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 ?

[blastbuilder]

I don't get why people always use unpredictable numbers like stock prices and sunspots to demonstrate neurol networks. You can't tell how good or bad the results are. It makes much more sense to use predictable data so we know which model works better for which types of data.