How can I train a single LSTM model with multiple datasets.

I have 1000 datasets of many devices eg: device1.csv.....deviceN.csv. I cannot merge them together because of varying values and time component although they share the same features.

Each dataset has device voltage with respect to its age. I want to train one LSTM model with all the datasets. Should I train in for loop?

Thank you! One thing I understand from that website is that they have taken resistance and discharge current as input for cell prediction. Which I do not have in my dataset. I need to check further.

I'm working currently working on a POC and since it's a non rechargeable battery, we do not have any information other than date and corresponding battery value. I don't know how to proceed further.

Yes thank you so much for answering. May I DM you for further queries?

I tried exponential regression. Didn't fit well.

My only question is that is predictive modeling necessary in these cases like battery lifetime prediction? Won't they be just static values when working with non rechargeable battery with no cycle life?

I'm working with a non linear data therefore traditional linear regression isn't working with capturing the trend.

If I had more features in my dataset, will it be better then?

I have already used regression algorithms like random forest and svr. I just wanted to know if machine learning is required at all.