I have time series data corresponding to different entities. My goal is to train a model on the set of entities I have, and then provided a new entity to predict the whole time series for it.
For example I have:
Provided an entity_3 and feature_1 over 1 year (let's say) the model needs to predict the entire time series over this year. I guess that technically this is not a forecasting problem but I have features across a time period for a new entity I never encountered and I need to predict what the target would be for it across the time period.
Now I am interested to predict the target as proportions using relative features, because the relationship between the feature_1 and the target can change according to the entity.
Now any model I use needs to be able to handle different timeframes, and if the timeline change the distribution of the relative features will change.
By different timeframes, I mean that sometimes the model can be asked to predict the whole time series over 1 year, or 4 month or kind of anything really. Because the entities can behave very differently I can't fit a model for each entity. So I need to have a model able to generalize between these entities.
For example: feature_1 at day 1 can be 1% of the sum of the feature over 1 year, but if my timeline changes to 6 month it can become 2%. Heard it's called compositional data.
So my question is how can I build features that would be relative but can be consistent over different time frames ? So if my model is trained on one year it can also be applied on 6 month ? Or would say I need a model for each time frame (one 3 month, 6 month, a year etc...)