I'm having a problem in understanding how to overcome this problem.
I'm aware that in order to apply various machine learning algorithm i can transoform a time series problem which is unsupervised into a supervised problem by providing lags of dependent variable https://machinelearningmastery.com/convert-time-series-supervised-learning-problem-python/.
But how I can produce this lagged variable in a real world scenario, when I'm trying to put my model into production? I'll make an example:
Let's suppose my model is a feed forward NN. I have a time series recorded on a daily basis. As a regressor I included the dependent variable lagged 30 days and other predictors extract from date( day of month, month, quarter, holiday etc etc). Now I'm at the last day of July and I want to predict my time series for the next 30 days. For example At time t+28 I should provide my model with lagged variable til t-30. But I don't have any data for the first 28 day of August. How should I proceed? I should take one step forecast into my model and using it as a dependent variable? I think that in this way i will introduce a substantial error in my model, if my predictions are not good enough.
Related to this issue: If I provide feature like a rolling window 30 days statistics ( let's say mean, std, or kurtosis) of my dependent variable, how should I compute this statitics? At time t+28, i should compute the mean of my data using 30 previous day, which I don't have.