I am building a model for classification on a dataset which is collected by recording a system's behaviour through 2 years period of time. The model is going to be used in the same system in real time.
Right now I am using the whole dataset (2 years) to build my classifier but I doubt it might not be the right approach. Since I am trying to model the behaviour of a system in real time, it is possible that older data points in the dataset have become irrelevant or uninformative compared to system's current environment (distribution of the system's input changing drastically through time for example).
My question is how can I determine which parts of the dataset to use for training, taking the last 1.5 years instead of all 2 years for example. Is there a statistical way to help me decide that a specific time period is not helping or possibly hurting the model to correctly classify more recent data points?