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
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!