I have a multi-dimensional data, but it misses target variable.
I want a model to predict a value for this data that could be passed to my loss function which can be then used as a feedback for updating the weights.
The simulation could look like -
- take one data-point, predict a value
- pass this value to loss function
- use that loss function as feed back
- update the weights
- In next iteration, take another data-point and predict value on updated weights
- again use this value in loss function for feedback.
- and the loop continues.
This way, I dont need labelled data for predictions. And I think my use case is more of time series, so can this be implemented with time series?