for my studies I am currently searching a suitable CL-algorithm dealing with a supervised regression problem with a continual data stream.
Task: The goal is to estimate the power of a production machine based on round about 100 input values. These input values are numerical and few are binary (one hot encoded). There already exists a big set of training data and models that estimate the regression problem really good. The input values are measured every second and occur in a continuous data stream.
Now it happens, that the task of the underlying machine changes (e.g. it is producing a part with a different shape), so the future incoming input data is not represented by the previous training data anymore. Therefore I am looking for a suitable algorithm to online learn the system with the incoming data stream(supervised). Genereally you can assume that it is known when the machine has to do a new task.
Most of the papers I found were dealing with classification problems for image processing. Is there any algorithm you can recommend for dealing with this problem?
How do you think about applying Elastic Weight Control for this problem? Do you think this could deliver good results?
Can anybody recommend an approach for this problem or can recommend an algorithm?
With best regards
MLStudent from Germany