Understanding Prophet's metrics
Prophet's diagnostics package provides six different metrics you can use to evaluate your model. Those metrics are mean squared error, root mean squared error, mean absolute error, mean absolute percent error, median absolute percent error, and coverage. We'll discuss each of these in turn.
Mean squared error
Mean squared error (MSE) is the sum of the squared difference between each predicted value and the actual value, as can be seen in the following equation:
Figure 12.1 – Mean squared error
The number of samples is represented in the preceding equation with n, while y is an actual value and ŷ a forecasted value.
MSE may be the most used performance metric, but it does have its downside. Because it is not scaled to the data, its value is not easy to interpret – the unit of MSE is the square of your y unit. It is also sensitive to outliers, although this may be either desirable or...
