Im trying to create a model to forecast demand/expenditure for an individual based on historical data of many individuals over time, but Im having trouble finding examples of this. To give a simple example of what Im trying to do:Lets say theres an ice-cream store that children in the surrounding area visit every week. Weve got the history of all purchases at the store and details of who made the purchase, so we can track the purchase history of each individual. We also have details about each individual, e.g. age, gender, school, no. of siblings etc. We can assume that a child will buy from the store once a week until they leave for good and stop purchasing.What I want to do is create purchase forecasts for each child over a few weeks. The bit Im struggling with is where a new child, say Timmy, has only been purchasing for a few weeks and so doesn't have enough of a history to forecast based solely on his own data (but we do have his details). My assumption is that demand can be better predicted by taking a child's details into account, so I'd like to find a group in the historical data that best represents Timmy, and leverage their data in some way when creating Timmy's forecast. Are there any standard methods/practices for this type of forecasting? Any advice or guidance would be much appreciated.
Forecasting an individual based on a representative group
machine learning;predictive modeling;forecast