# How to calculate price prediction model accuracy from metrics such as MAE and MSE

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!

Code:

from sklearn.metrics import mean_absolute_error
mean_absolute_error(y_test, preds)


result: 6415.621912643506

• Welcome to CV and congratulations on your first post. Don't think of your model as a lookup table, a single line, where you put in a single input and get out a single output. Think of it as you put in a single input, and then it puts out a distribution of values, like a bell curve. Your fit tells you the "highest point" of that bell curve, the most likely of many likely values. You need to consider the sides, the prediction interval. That MAE tells you more about the spread. It gives you a sense of how far off your model is most of the time; how fat or narrow your distribution is. – EngrStudent Jan 10 at 13:07