I am new to both statistics and machine learning in general. I've tried to construct a price prediction model using the RNN-LSTM architecture. For this problem I have a dataset of one-minute closing prices of Bitcoin. The goal of this project is to quantify the accuracy of the prediction model. And this is my issue. I built the model, plotted it and applied the MAE metric, but I am not sure what to make of the resulted value, or rather how to compute the actual accuracy of the model in the range of 0-100%. The closing price values range from roughly 6670-6770 Is this possible? I would very much appreciate any suggestions. Thank you very much!
from sklearn.metrics import mean_absolute_error mean_absolute_error(y_test, preds)