Let's say I want to predict whether a company will default on it's debt at some point in time (so binary classification) and one of the time series variables I'm using is the "revenue" of that company thought time. Can I binned this variable "revenue" using quantiles cut (like so => pd.qcut(df['revenue'],bins=10)) without creating a data leakage ?
I'm under the impression that I can not really do that since the quantile cut is made by knowing the entire distribution of the variable "revenue" throughout the period. That is, the bin attributed to "revenue" at any point in time in my training data will carry information about the future.
Am'I correctly assuming that this will create a data leakage for this time series prediction problem ? If so, can I safely use pd.cut instead with no quantile ?