I have a regression problem in which for each week I have several rows (variable between rows i.e 1 week might have 1800 rows and other might have 5000 rows).
My target is to predict a value at end of each week's data. Here's an example of what I need : x is a feature y is the target.
Week 1 ; x1, x2, x3.. x90
Week 1 ; v1, v2, v3... v90
.... 100 more rows
Week 1 ; z1, z2, z3... z90
Week 1; y1
Week 2; a1, a2, a3.. a90
Week 2; c1, c2, c3.. c90
.... 500 more rows
Week 2; r1, r2, r3.... r90
Week 2; y2
And so on..
I have tried aggregation to weekly values but the results are too bad to be believable, the best model could make a guess at random.
The target values also contain a lot of zero's much like zero-inflated data.
Can you guys help me format this problem into a solvable format and any recommendations for the models to use for such a data?
Total number of weeks are around 120 and total number of feature for each instance (rows) are around 90.