  We have analyzed a limited sample set of 120 GHz, and 150 GHz time-resolved
millimeter wave (mmW) photoconductive decay (mmPCD) signals of 300 nm thick
air-stable encapsulated perovskite film (methyl-ammonium lead halide) excited
using a pulsed 532-nm laser with fluence 10.6 micro-Joules per cm-2. We
correlated 12 parameters derived directly from acquired mmPCD kinetic-trace
data and its step-response, each with the sample-age based on the date of the
experiment. Five parameters with a high negative correlation with sample age
were finally selected as predictors in the Gaussian Process Regression (GPR)
machine learning model for prediction of the age of the sample. The effects of
aging (between 0 and 40,000 hours after film production) are quantified mainly
in terms of a shift in peak voltage, the response ratio (conductance
parameter), loss-compensated transmission coefficient, and the radiofrequency
(RF) area of the transient itself (flux). Changes in the other step-response
parameters and the decay length of the aging transients are also shown. The GPR
model is found to work well for a forward prediction of the age of the sample
using this method. It is noted that the Matern-5 over 2 GPR kernel for
supervised learning provides the best realistic solution for age prediction
with R squared around 0.97.
