Is it necessary to make time series data stationary before applying tree based ML methods i.e. Random Forest or Xgboost etc?

Question

As in case of ARIMA models, we have to make our data stationary. Is it necessary to make our time series data stationary before applying tree based ML methods? I have a dataset of customers with monthly electricity consumption of past 2 to 10 years, and I am supposed to predict each customer's next 5 to 6 month's consumption. In the dataset some customers have strange behavior like for a particular month their consumption varies considerably to what he consumed in the same month of last year or last 3 to 4 years, and this change is not because of temperature. And as we don't know the reason behind this change, model is unable to predict that consumption correctly. So making each customer's timeseries stationary would help in this case or not?