Understanding AC_errorRate loss function I'm reading an article about Rolling Window Regression: a Simple Approach for Time Series Next value Predictions.

He explains about 5 different loss functions. I managed to understand the first four, but I don't understand the fifth one:


  
  Almost correct Predictions Error rate (AC_errorRate) — the percentage of predictions that is within %p percentage of the true
  value
From my understanding, this loss type means:


you define a threshold percentage error (let’s say 2%)
for each true value y, the desired prediction should be between y + 0.02*y and y - 0.02*y
the percentage of predicted values fulfilling the rule above contribute to the “inliers” predictions, I.e., good predictions


This idea reminds me to what RANSAC does