# Statistical method for analysing time series with correlated datasets

I have a time series of different physical measurements. One of these, lets call it X, is determined by the others, $Y_1$...$Y_n$, through various physical processes (which are complex and not fully known). There is no influence of X on Y. The Y parameters are all caused by the same source, so they are correlated to various degrees. Some of these correlations are close to 1 or -1.

I am looking for a statistical method to analyze the impact of the different parameters on X.

PCA was suggested, but I have not used it before and from what I read it seems not well suited to the problem because of the correlation between the input parameters.

It might be possible to reduce the Ys to a combined parameter for each physical process (where correlation is lowest between them) and then use PCA, but I would be interested if there is a statistical method better suited to my problem, where I can use all parameters. Maybe someone here has an idea?

• You should apply PCA on Y, and then check correlation of different factors to X. Oct 19, 2016 at 6:59
• Thats a good point actually, thanks. Maybe I should do this with all subsets of parameters that relate to the same physical process. Oct 19, 2016 at 8:45