I'm playing with machine learning and LSTM. My goal is to learn something new and work with real data. Currently, I'm trying to predict bitcoin price. I have understood the necessity to normalize data, but there is something that I don't get. When I use a scaler like MinMax (or any other scaler as I understand it), I give my train, validation and test sets to the scaler and I get a range of value easiest for tensorflow to work on.
After that, I can train my model and save it for later with model.save.
For this example, let's say that I trained my model on the 2021 (2021-01-01 to 2021-12-31) data for bitcoin price and the minimum price was \$20_000 and maximum was \$50_000.
Now it's 2022 and I got new data and I want to use my model saved last year for predicting the future! Let's say that minimum price here is \$15_000 and maximum \$55_000.
If I load my old model and add my new data (and normalize them) to it, the scaler need to be updated. How can I do that? I only find some basic tutorials with "finish dataset" or temperature prediction but not how to feed my model with new data.
I hope you understand my problem and that you will guide me to a solution. I can share my code if you want more information on my workflow.