It isn't meaningful to run PCA on a univariate time series (or, more generally, a single vector). To run PCA on time series data, you'd need to have either a multivariate time series, or multiple univariate time series. There are ways to transform a univariate time series into a multivariate one (e.g. wavelet or time-frequency transforms, time delay embeddings, etc.). For example, the spectrogram of a univariate time series gives you the power at each frequency, for each moment in time.
Say we have a multivariate time series with $p$ dimensions/variables. Or, we might have a set of $p$ univariate time series, where each time point has some common meaning across time series (e.g. time relative to some event). In both cases, there are $n$ time points. There are a couple ways to run PCA:
Consider each time point to be an observation. Dimensions correspond to variables of the multivariate time series, or to the different univariate time series. So, there are $n$ points in a $p$ dimensional space. In this case, eigenvectors correspond to instantaneous patterns across the dimensions/time series. At each moment in time, we represent the amplitude across dimensions/time series as a linear combination of these patterns.
Consider each variable of the multivariate time series (or each univariate time series) to be an observation. Dimensions correspond to time points. So, there are $p$ points in an $n$-dimensional space. In this case, the eigenvectors correspond to temporal basis functions, and we're representing each time series as a linear combination of these basis functions.
Given the above, it's apparent why PCA doesn't make sense for a single univariate time series. Either you have $n$ observations and 1 dimension (in which case there's nothing for PCA to do), or you have a single observation with $n$ dimensions (in which case the problem is completely underdetermined and all solutions are equivalent).