I am working on the calibration of low-cost air sensor data (a time series regression problem). My primary focus is to use some meta/ few-shot learning approach to solve this problem with fewer data. I have tried using MAML on top of LSTM/vanilla NN but the results are not satisfactory.
Is there a different approach/paper for meta-regression? Anything that I should be doing differently? Things to avoid?