Do I need stationary time series data for Isolation Forest Model? I am trying to predict anomalies using an isolation forest model with daily time series data. Do I need to make sure my data is stationary as I have observed weekly seasonality? I read that you need stationary time series data for forecasting and predicting future events, however, an isolation forest model is not technically forecasting.
Generally, if you are using time series data for a machine learning model, when does your data need to be stationary?

Tree based algorithms may struggle with non-stationary time series data. Especially when there is trend present, the future values of the series may have a very different distribution. For seasonality, this can be addressed by including seasonality factors in the features. But, it is easier for the forest to isolate samples when the effect of trend and seasonality are factored out. Generally speaking, a method should have the ability to (even implicitly) deduce trend and seasonality somehow to deal with this kind of data (e.g. SARIMA).
