Forecasting revenue - what and how to pass input I have a dataset with quarter wise revenue for past 3 years from Jan 2020 to Dec 2022. I have 4642 customers.
Each customer has 1 row of data which includes features based on his purchase frequency, avg revenue, total revenue, min revenue and max revenue etc for each quarter.
So, from Jan 2020 to Dec 2022, we have 12 quarters and data for all those quarters.
So, now our objective is to predict the revenue for 2023Q1.
As most of our customers have zero revenue, I use Zero Inflated regressor(ZIR).
So, I built a model with training set (2020Q1 to 2022Q3) and testing set (2022Q4).
But the training was done after some feature selection which resulted in omission certain quarter characteristics.
So, my important variables were Avg_revenue_2021_Q4, AVG_REVENUE_2022_Q2_trans_rate_2022_Q1, Total_revenue_2022Q3
So, now that model is built and validated, I get a R2 of 89% and 87% in train and test set respectively and decent MAE scores.
But my question, how do I predict the revenue for 2023Q1? 2023Q1 hasn't happened yet (and we need to predict in advance for the same).
So, should I build my final model by combining my train (2020Q1 to 2022Q3) and test (2022Q4)?
Once I build this final model, what is the input that I should pass to get predicted values for 2023Q1? there is no test set because 2023Q1 hasn't happened yet.
So, now to predict for 2023Q1 (for each of the 4642 customers), should I again pass the values of Avg_revenue_2021_Q4, AVG_REVENUE_2022_Q2, Total_revenue_2022Q3
?
Or should I shift them by one quarter (and pass values for Avg_revenue_2022_Q1 and Avg_revenue_2022Q3) because our prediction is for one quarter later? (2023Q1)?
I know we could have used techniques like ARIMA etc but now that I have used ZIR, I would like to know how can we predict for the future quarter (next quarter).

Your question implies that you are forecasting for multiple customers simultaneously.   If this is the case then you actually have longitudinal data - repeated observations of the same units (customers).
There are numerous different models for longitudinal data (e.g. fixed effects, random effects, mixed effects).  Because your problem is forecasting rather that inference you should review the assumptions and data constraints, e.g. some estimators require complete data.
Finally, regarding your question of your test data: your training set should yield estimators, and you test these on your test data.  Applying these to your forecast periods then gives you your forecast.  Do not combine your test and training data - that will result in a different model.
However, you should also consider withholding a group of customers - you are  modelling across customers and not just time.
