Consolidating multivariate time-series information from many data sets I am having trouble setting up a problem with regards to time series analysis. I have 30 data sets, where each set corresponds to a certain project. Each project has 7 features, and each feature has time series information sampled every week from 2018 to today.
One of the features is how much the project is under/over budget and I wish to use this as the label.
If I was learning on a single project it could be a straight forward multivariate time-series. As an example I could transform the data from this:
Week   X1   Y1
1      0.5  3
2      1    5
3      1.5  8

to this:
X1   X2   X3   Y
-    -    0.5  3
0.5  3    1    5
1    5    1.5  8

Then I would use the second table as my input data. However, with 30 different projects all with the same time-steps I'm not sure how to combine this information so a single model could learn it. One solution I thought I could do was a bagging approach. I would train 30 models and I could do a voting/weighted average for predictions, but I feel like this isn't the best approach. If anyone has dealt with a problem like this before, please let me know. Thanks in advance!
You could put all Information into 1 table with 30*7 features, since your timeline is the same for all tables.
Then, in order to predict the under/over budget, you can build a model with 30 output-nodes, where one node indicates whether a certain project was under/over budgeted.