Body: <p>I'll try to rephrase my problem in the context of video processing. Imagine that initial frame of video has some translational symmetry. The frame evolves according to an update rule. </p>
  
  <p>I generate a time series for how an edge, say right up edge, of the frame evolves. I generate another time series for how a larger edge, including the smaller right up edge, evolves. Since there is translational symmetry, I should be able to find how the smaller edge is related to the larger edge. The final goal is to use the obtained correlation to extrapolate to larger edges. I want to find the correlation between these two multivariate time series using machine learning (ML) methods. </p>
  
  <p>I want to know</p>
  
  <p>1 - which one of ML methods can be used in general for this task?</p>
  
  <p>2 - if I use neural networks, the input and output shapes would be <code>(values at time steps, number of variables)</code>. For the input it makes sense, but how can I define the output layer (for example, for LSTM in tensorflow)?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-01-26T01:24:22.913Z
Comments.Text: Surely the sample correlation is just a direct calculation, with no ML needed.What do you mean by "correlation" ? And why do you think ML is needed?
Comments.UserDisplayName: Peter K.
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-01-26T08:03:17.347Z
Comments.Text: @PeterK. I am not sure if the correlation is the right word. I updated the post to make it clear.
Comments.UserDisplayName: New Developer
Tags: <machine-learning><recurrent-neural-networks><time-series><forecasting>
Title: How to exploit translational symmetry for extrapolation in video generation using machine learning
UserDisplayName: New Developer
ThreadText: # How to exploit translational symmetry for extrapolation in video generation using machine learning
  
  (Asked by: username_0 on 2020-01-25)
  
  I'll try to rephrase my problem in the context of video processing. Imagine that initial frame of video has some translational symmetry. The frame evolves according to an update rule.
  
  I generate a time series for how an edge, say right up edge, of the frame evolves. I generate another time series for how a larger edge, including the smaller right up edge, evolves. Since there is translational symmetry, I should be able to find how the smaller edge is related to the larger edge. The final goal is to use the obtained correlation to extrapolate to larger edges. I want to find the correlation between these two multivariate time series using machine learning (ML) methods.
  
  I want to know
  
  1 - which one of ML methods can be used in general for this task?
  
  2 - if I use neural networks, the input and output shapes would be `(values at time steps, number of variables)`. For the input it makes sense, but how can I define the output layer (for example, for LSTM in tensorflow)?
  
  **Tags:** machine-learning, recurrent-neural-networks, time-series, forecasting
  > **Comments:**
  > **username_1**: Surely the sample correlation is just a direct calculation, with no ML needed.What do you mean by "correlation" ? And why do you think ML is needed?  
  > **username_0**: @PeterK. I am not sure if the correlation is the right word. I updated the post to make it clear.  
  
  ---
  
  ## 0 answers
  
  