Prediction Intervals in Forecasting: Quantile Loss Function
In most real world prediction problems, the uncertainty in our predictions provides significant value. Knowing about the range of predictions as opposed to only point estimates can significantly improve decision making processes for many commercial applications.
What is a prediction interval?
A prediction interval is a quantification of the uncertainty on a prediction. It provides a probabilistic upper and lower bounds on the estimate of an outcome variable.
Taken from “Machine learning approaches for estimation of prediction interval for the model output”, 2006.
Although, most model outputs are accurate and close to the observed value, the outputs are themselves random variables, and thus have a distribution. Prediction intervals are necessary to get an idea about the likeliness of the correctness of our results. This likeliness determines an interval of possible values.
Quantile Regression Loss function
Machine learning models work by minimizing (or maximizing) an objective function. An objective function translates the problem we are trying to solve into a mathematical formula to be minimized by the model. As the name suggests, the…
