I have a time series that deals with rainfall. It is a period of 10 years (daily resolution), and covers climate variables.
I'm going to feed the data into an Artificial Neural Network to predict the rainfall variable (PP).
As what I've been reading, MAPE's formula involves dividing by the actual observed value. But since its rainfall, there will be days with little or zero precipitation values.
This is bad (dividing by zero = black hole). So how am I going to go about this? I could do data replacement on the zero or close to zero values, but that's stupid - if I do that, I inflate a lot of things, and am pretty much tampering with the data in a way (unlike missing values, which should be imputed by way of other data and not filled in with some other arbitrary value).
My professor is stubborn as a mule. Is there any alternative to MAPE? Or are there any methods to circumvent the issues of MAPE?
THERE ARE SMALL AND ZERO VALUES IN THE DATASET... Am I just screwed now?