Time Series Data Encoding for Deep Learning, TensorFlow and Keras (10.1)

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Time series data is usually represented in the form of sequences when working with Keras and TensorFlow. In this video sequences are introduced for time series prediction.

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Комментарии
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I have been binge watching your videos. They are simply brilliant. Thank you.

esra_erimez
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I haven't done any serious time series modeling yet, just a few small projects using linear regression in scikit-learn. This video made me curious about the kind of performance you can get with LSTMs in TensorFlow. I'll give it a try. 😊 Thank you for another useful video.

BiancaAguglia
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Correction: In each neuron u pass all the features, not every single feature is passed seperately in every single neuron.

smilebig
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how would you encode a dataset containing multiple time series? like a series for temperature and one for light level.
I would like to use an autoencoder on this type of data.
For now, I am only concatenating all the values in a single using a min-max scaler on related columns.
If I plot the decoded time series they are not very similar to the input ones.

cunningham.s_law
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"encoding" not a great title choice. more like "shaping"

LayneSadler
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Can you suggest any method I could use to machine learn a 2D time series output ?

I am trying to learn the movement of a biofilament based on its Stiffness/velocity/ amplitude.

I have a model that can generate this data, I was hoping to learn to avoid the slow model.

Is it possible to ml

Input (amplitue/stiffness/time) ---> (x-y) movement in time

snaidu
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Hello, thanks for all but I have a request please use real data instead of creating vectors like X you start by two value and after that, you go back to one and you don't explain well how reshaping data to get the right dimension.
Thanks again.

maloukemallouke
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