machine learning algorithms (Xgb, LSTM, others) for time series forecasting 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. 
 A: *

*"ML" approaches will typically do better in high signal to noise situations, and with enough data. With less data, the traditional forecasting approaches are often superior.

*This is a very broad question. One recommendation: familiarize yourself with the state of the art in "classical" forecasting. I recommend the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman. Test your ML forecasts against simple benchmarks, which surprisingly often outperform more complex algorithms.

*One way to deal with trend is to include a linearly increasing predictor and extrapolate that out. Forecasters often find that dampening the trend improves forecasts. Similarly, you can include periodic functions like harmonics or bump functions as predictors to model seasonality.

*There are many accepted measures of forecast accuracy. You may also want to look through the tag wikis on the MAPE, the MASE, the MAE or the MSE, or through threads tagged both "forecasting" and "accuracy".
