First off, I have very limited knowledge statistics-wise and am more of a coder. I was thrown into a large scale project and could use some guidance.
I have a large multivariate time series dataset that I'm working with, where I'm trying to predict when an error will occur. There are several errors that could occur, however, I'm am trying to predict when only a certain error
A will occur. The model I'm looking into is Recurrent Neural Network.
The data looks something like this:
time param1 param2 param3 ... param30000 | error 0s 30.202 9.2102 0 .201120 | NULL 1s 30.202 11.177 1 .249165 | NULL 2s 30.202 12.293 0 .171295 | NULL . . . 1930s 23.246 10.372 1 .302009 | A
Obviously I plan to reduce dimensionality, but how could I tackle this problem to predict when
A would happen again? How should I be structure the data? Time lagging? Any hints/guidance would be appreciated greatly.