I have seen many kernels that are using machine learning algorithms (Xgb, LSTM, others) on time series forecasting.
A time series data typically has trend and seasonal components. In general my question is
Is it necessary to remove trend and seasonality (i.e make it stationary ) before applying machine learning/supervised learning (Xgb, LSTM, others) algorithms for time series data ?
when will machine learning/supervised learning (Xgb, LSTM, others) algorithms for time series data gives good result? When will they not give good result?
Any guidelines for using machine learning/supervised learning algorithms for time series data ?
If there is seasonality and trend how to tackle the problem? One way is to detrend and remove seasonality, and then use ML algorithm for forecasting.
Are there any other approaches especially if time series has trend?
Finally, How will you verify the forecast results? I mean, can we look at the residuals/something to infer forecast results makes sense.