I have a dataset of sample concentrations of several ions/elements (Ca 2+, SO4 2-, K +, S, Ca, V, etc.) from an ice core which I am using to detect volcanic eruptions. The raw version of the dataset has a high resolution (many samples per year) for recent years and an increasingly low resolution for older samples. I also have a resampled version of the dataset, where the data points are interpolated at a regular time interval (quarter-year frequency), for my other analyses, as most types of time series analyses assume that the data is collected at regular time intervals.
I would like to perform an EOF (Empirical Orthogonal Function) analysis on the dataset, because a mode that has a high sulfate loading may represent volcanic activity. I read somewhere (sorry, I don't remember where exactly, but I think it was on Cross Validated) that the even spacing of the time series is not required for EOF/ Principal Component Analysis, but I am not sure why.
Now my question is: for my EOF analysis, should I use the raw data (the unevenly-spaced time series) for maximum precision of my results, or should I use the resampled data (linearly interpolated data points at a quarter year frequency) in order to avoid all the problems entailed by an uneven time series? (I am not sure exactly what those are, but I imagine problems like overestimating trends based on recent observations or underestimating trends present in older observations)