I want to fit a machine/deep learning model to fit below table data format F1-F5 are features and Y gets captured is a random time interval.
f1 f2 f3 f4 f5 y
11 12 13 14 15
.
.
.
n1 n2 n3 n4 n5 2.3
n+11 n+12 n+13 n+14 n+15
.
.
n+x1 n+x2 n+x3 n+x4 n+x5 3.5
I am confused to go whether to go with CNN kind of approach, multiplying 1*5 sized kernel through every row from 11-n1 and try to learn by matching sum with Y. Kindly suggest me how to tackle such data.