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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)?

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  • $\begingroup$ 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? $\endgroup$ – Peter K. Jan 26 at 1:24
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    $\begingroup$ @PeterK. I am not sure if the correlation is the right word. I updated the post to make it clear. $\endgroup$ – New Developer Jan 26 at 8:03

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