How can we predict a value after several rows of data?

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.

Topic multi-instance-learning aggregation time-series data-cleaning machine-learning

Category Data Science

1
answered at

Try to make all the rows of the week one row.

Considering the max number of rows/weeks:

Week 1; $x_1, x_2, x_3...x_{90}, v_1, v_2, v_3...v_{90}...z_1, z_2, z_3...z_{90}; y_1$

Week 2; $x_1, x_2, x_3...x_{90},v_1, v_2, v_3...v_{90}...z_1, z_2, z_3...z_{90}; y_2$

$y_i$ depends on all the rows of the week.

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