I guess I understand the idea of predictions made via LSTM or XGBoost models, but want to reach out to the community to confirm my thoughts.
This tutorial does a nice job explaining step by step of what to do: "How to Develop Multi-Step LSTM Time Series Forecasting Models for Power Usage"
However, when it came to forecasting, the author held out portion of the data and then used that data to predict future values... In my mind, it is not really a forecasting (something that is done via ARIMA, VAR, etc - where you specify number of time periods and then don't specify anything else and the model gives you some forecasting for the future) You can see my comments to the author and basically the predictions via LSTM or XGBoost models will be based on the input values and not on the future data range, i.e. I would need to supply temp, humidity, wind, etc to get the forecast of the power consumption for the house hold.
That being said, in what extend LSTM or GXBoost are used in forecasting? Given these values predict what would be the final outcome? Hence if I need to forecast something in the future and have no clue about the other input values, just stick to traditional VAR model?
By looking at this question/answer by @Fnguyen, "If you have more input variables you need a way to forecast or impute these because to make a prediction your model needs all inputs that build the model. " it seems that I should use ARIMA to forecast other input values and only then use LSTM... makes me wonder if forecast would be that accurate now that it depends on ARIMA that would predict future values of the input of LSTM and then LSTM model that would make final prediction. Then again, accurate forecast is fairly complicated thing to do as we are trying to predict the future.