PCA on one time series variable into components

I tried to use PCA from MultivariateStats, and I got a little bit stuck.

I have a one-time series variable - Y.
I need to decompose it into orthogonal factors (by using PCA).
It looks like a number of factors can be larger than the dimension.

Can anybody help me, please?

To the best of my knowledge, that can’t work. In a time series context, you can think of PCA decomposing the variance-covariance matrix of several time series into a (potentially smaller) set of orthogonal factors that best explain the time series variation of all series simultaneously. If you have only one time series, that is your factor.

I should add that you could still potentially do such a decomposition, but you need some other data+restrictions to discipline it. A Kalman Filter perhaps could be one substitute?

Thank you for the answer.

I am sorry, I’ve got my task in the wrong way.

Look at: GitHub - baggepinnen/SingularSpectrumAnalysis.jl: A package for performing Singular Spectrum Analysis (SSA) and time-series decomposition
For a single time series, it’s “PCA of the time series trajectory matrix”