Time Series with convolution layers?

Hi all,

I have dozens of process variable time series, and for one set of those process variable time series I have only one result, one number. Unfortunately, I don’t have a lot of examples to train my model. So lots of input data, and not a lot of output data. I am wondering what is the best approach to train a model in such cases? I the second week of the machine learning specialization I learned about convolution layers, and there I thought to myself, perhaps I could tackle such cases with convolutional layers, because it reduces dimensionality. I am looking just for some general answers, or way pointers. I appreciate your response.

Best Regards,
Rok Bohinc

Can you create some data that follow your certain pattern or maybe can you label the data you have so you can have inputs and outputs for them.

So I have about i=1-20 variables (floating points), and for each variable I have a time series with roughly j=1-500 time points. The variables are measured simultaneously, so the first time point j=1 is (Date Time) the same for all the variables the same, and they are of course correlated. So I have roughly 20*500=10000 data points per example. I have k=1-300 example points. The inputs are then x_ij^k. Per example, I have a floating point result ranging from 0 - 100, y^k. Does this help?

To be honest with you, you lost me halfway on it, sorry…if you dont have labels you have to come up with some labels I dont know what much other to tell you.