I am currently working on a project where i need to predict next 4 quarters customer count for a retail client based on previous customer count of last three years i.e. quarterly data means total 12 data points. please suggest a beat approach to predict customer count for next 4 quarters.
Note:-I can't share the data but Customer count has a declining trend YOY.
Please let me know if more information is required or question is not clear.
With only 12 data points you would be hard-pushed to justify anything more than a simple regression analysis.
If the declining trend was so strong that you were at risk of passing below 0 sales you could look at taking a log to linearise the data.
If there is a strong seasonal cycle you will need to factor that in, but doing so also reduces the effective sample size from 12 to 9 quarters of data (three degrees of freedom being used up by the seasonalisation).
Thats about it really.
You dont specify explicitly how far in the future you want to make your predictions, but rather you do that implicitly when you make sure your model is robust and does not over-fit.
What does that mean?
Make sure that distribution of labels with your available independent varaibles has similiar distributions of that what you expect in future. You cant expect your model to learn patterns that were not there in the first place. So variables that show same information for distinct customer count values 4 quarters in the future are what you want to include.
