I'm getting data by connecting to database to get electricity consumptions. In my case there are lots of meter id's and each meter id has its own time series hourly data. Since some of them show similar patterns and some dont, i decided to train them separately. But the problem is while using XGBoost i must create the best features for each time series and thats a hard thing to do. What should be the approach? Maybe i should train them in a single model for easy feature engineering but this time model will have noise?

I'm getting data by connecting to database to get electricity consumptions. In my case there are lots of meter id's (they will increase to thousands in time) and each meter id has its own time series hourly data. Since some of them show similar patterns and some dont, i decided to train them separately. But the problem is while using XGBoost i must create the best features for each time series and thats a hard thing to do. What should be the approach? Maybe i should train them in a single model for easy feature engineering but this time model will have noise?