Recursive time series forecasting model In a recursive forecasting model, let's say you are trying to predict sales of Target for the next month and you will append that prediction to your input and predict the month after. Basically, your target is Sales quantity, which you lagged. But let's say you have other continuous numerical features like Price which you will lag as well. As your model is making predictions on Sales_QTY, it will feed it back to sales_qty as input so you will never ran out of values for that one. So how do you deal with your other features (other than the target) in order to generate them as next months inputs in a recursive forecasting model (because eventually you will run out of them)? DO you create a sub-model and try to predict them as well?
 A: A standard approach would be to train direct multi-step models (DMS) for each forecast step instead of a recursive model. So you would train one model to predict $y_{t+1}$, another model to predict $y_{t+2}$ and so on.
Advantages:

*

*no need to forecast additional variables that might be hard to forecast, e.g. stock level

*no mix-up between actuals and predictions

*short-term models can use different variables than the long-term models, e.g. today's sales and stock is important for the short-term models, but general trends and product lifecycle are more important for the long-term models

A: 
I tried both techniques but I was looking for something better

There is no magic way around the fact that the future values of explanatory variables are unknown, just as the future values of the variable of direct interest are unknown. You could use a vector autoregression to forecast all of the variables together or (as Tylerr suggested) have individual predictive models for each variable. Or if you have expert forecasts available, you could use those, too.
Also, note what Chris Haug points out: including an explanatory variable that is hard to forecast is not guaranteed to improve the forecast of the variable of direct interest. Only if you can forecast the explanatory variable with sufficient precision may it be worth retaining it in the predictive model.
