I had a question in regards to PCA with times series data, and specifically how to possibly interpret it. Normally, PCA is used by other software that I use in relation to de-noising a data set by removing components of least eigenvalues/variance (not sure as to the precise criteria used here), and the de-noised data is passed along for other preprocessing steps.
However, I decided to manually use PCA on my data and actually get a look at the results of doing so. Essentially, I have 5 variables (5 brain regions), each with 180 time points, with specific intensity values (unit is same across the 5 variables).
Now, my experience with PCA is limited, but I was wondering if there are any potential interpretations that could be done with such a time series data set?
From what I understand, the 4 variables (NOT the Cerebellum) are highly related to the PCA axis 1, and these 4 (RightPM, RightM, LeftPM, leftM) can be regarded as positively correlated with each other in relation to the intensity values? Confusion sets with the time points however, such as saying whether this strong positive relationship occurs at the time points plotted near the bunching of the 4 variables.
I'm assuming there are issues with PCA and time series (related to autocorrelation perhaps), but I was curious if such an interpretation could be made. Any opinions on this would be great.