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"Window Optimization for Multivariate Time Series Analysis"

User: "jqford"
New Altair Community Member
Updated by Jocelyn
I would like to optimize my windowing scheme for a multivariate time series analysis. 

An important feature I would like to explore in my analysis is whether the window size should be optimized on an individual basis for each attribute.  For example,  my data suggests that the past five measurements of "Attribute A" are relevant to future predictions, but only the past three measurements of "Attribute B" are important.  I am concerned that the use of two extra measurements of "Attribute B" by the learner will leads to increased forecasting errors.

I have noted that MultivariateSeries2WindowExamples only permits a single window size in its transposition of data.  Using the example above, this would lead to the inclusion of additional measurements of "Attribute B", which would negatively impact my forecasting performance.

Is there a way to specify a unique window sizes for each attribute within MultivariateSeries2WindowExamples?  Are there any other tools / processes that would allow me to optimize window sizes for individual attributes?

In addition, I am wondering whether anyone else has used such a scheme.  I am sure there is a paper out there somewhere on this issue - but I have not located it.  Any suggestions?  I'm not afraid to do some dusty-book research on this if needed.

Thanks in advance for any input!

Josh

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