Comments (6)
A few observations.
- This assumes that the dataset
X, y
needs to be sorted and needs to represent a single timeseries? Is this the intended behavior? - What is reasonable for k=0?
- How many steps ahead might be reasonable to predict?
- Can you come up with a use-case?
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Indeed, X and y have to be sorted time series.
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Not sure if I understand the question correctly? For k=0 you insert the first element in your time-series, e.g. the "initial condition".
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In theory; infinite. In practice: depends on how well the fit is.
Compared to a linear model (Note that if A, B & C are 0 the formulation reduces to a "standard" linear model y = D * u) it should perform better as it learns time-domain dynamics. I wouldn't be surprised if you can achieve the same result by adding delayed features...but that is manual work ;).
- Marketing efforts to sales predictions
- Production line (or any system that has "B can start after A is finished" types of rules) disturbance analysis.
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in a way, it sounds as if you are going to implement an RNN.
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i might be open to something like this being a part of scikit-lego via a dependency. but i kind of want to be careful with how many dependencies we add to the project.
thoughts, @MBrouns ?
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I wasn't planning on implementing a RNN. That would be equivalent to using a neural network to implement a linear model: possible but a bit overkill. :)
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something tells me that this will be really hard to implement if you consider the train/test aspect of it. ill close this issue for now (but feel free to challenge me on this)
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Related Issues (20)
- [DOCS] Example usage in docstring HOT 16
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