I am trying to classify the state of a machine using different features coming from a set of sensors.
I am treating the problem like a time series, so I windowed the stream of the sensors each X seconds so that every sample has size ( X * s_f ).
I am obtaining fairly good results using a CNN but I would like to increase performances using some kind of approach that understands the patterns between different states.
For example if going from state A to B is more frequent than A->C I want the algorithm to include this in the classification.
My idea is to classify each sample taking into account the classification of the previous sample. How can I do this?