I need some hints for the following task: I got two time series from different weather sensors which contain daily rain data values. One of the sensors is a professional one and my job was to build a low-cost sensor and compare the results with the professional one, which is supposed to be very accurate.
When facing a nonstationary time series the normal procedure would be to decompose it and derive a model out of it. As rain being not really time dependent in non tropical areas i would guess that rain data is already stationary.
How would i proceed then? I was thinking about something like this:
1) Proofing stationarity of the time series
2) eliminate outliers
3) Correlation (Pearson) of both time series
I'm really new to statistics so this might be a really noobish approach. Thanks for suggestions.