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 a lesser amount of data. I have tried using MAML on top of LSTM/vanilla NN but the results are not convincing. Is there a different approach/paper for meta-regression? Anything that I should be doing differently? Things to avoid etc? Thank you in advance.
In this paper the authors recast MAML and Multimodal MAML for time series regression by creating "meta-windows", which are a group of rolling windows and are considered proxies for time series tasks.