TensorFlow Tutorial #23 Time-Series Prediction
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How to predict time-series data using a Recurrent Neural Network (GRU / LSTM) in TensorFlow and Keras. Demonstrated on weather-data.
https://github.com/Hvass-Labs/TensorFlow-Tutorials
This tutorial has been updated to work with TensorFlow 2.1 and possibly later versions.
Source
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The only understandable tutorial on this topic I've found. No one else knows to explain the reasons why they do what they do I guess, so that I can actually fucking learn it, thank you!!!
people from Denmark and Norway are very intelligent. They have monster brains
I want to classify anomaly detection using RNN keras.tf but I have a problem where the accuracy value increases but the val_accuracy value does not change and just remains constant at 50%. this is my complete code available on google colab https://colab.research.google.com/drive/1saoNuCxj08JCxZ_7taIjhp8sJEIV-T5U?usp=sharing //
With this implementation the predictor variables are available at all time steps in the forecast, but the forecasted variables are not, right?
Can I use this for a dataset with less than 20 records ?
I have paid 200 usd to study these lessons at school. Now, I can learn tl on this channel and they are free.
God bless you.
Still very good material, and going much more in depth than other sources. Kudos!
Hi Magnus,
Excellent tutorials on TF TS. I am not able to find the weather data for Denmark in the link NCDC as mentioned. Also the import inclusion – import weather – is not having any effect. No download / import is happening. Please help me on how to get the weather data. Regards, KM
thanks for sharing. Please upload more content!
I’m having error after running In83 as profiler not running error
Can you help me?
So glad I found this video! A question if I may, does anyone know how to only use the last 60 days to predict the next day, rather than the entire prior data?
Good tutorial. Aweful results.
https://www.tensorflow.org/tutorials/structured_data/time_series
can you explain this code !!
Thanks for the tutorial. I may not hear so many "here(s)" anywhere else except here.
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Many thanks for this video Magnus, learned a lot – callbacks were amazing! One observation re the custom loss function. You've built that to allow a warm up period, statistically makes a lot sense. I tried implementing a warm up period in a different way in my own model, using sample weights by adding this code to model fit "sample_weight=np.concatenate((np.zeros(20), np.ones(len(x_train_scaled)-20)), axis=0)". This could be a more robust way of doing it, as it doesn't seem to cause NaNs when input dimension or batch size changes.
if you're using the past data to predict future weather, why is the y_data also shifted as the x_data did
good tutorial, but have you excluded your y variable from your x_data?
Great tutorial my friend
Hello Erik, Can we fill out the missing data by using LSTM ???? and How ???