PCD - Prediction of Change in Direction
\[\text{PCD}(y, \hat{y}) = \frac{1}{n-1} \sum_{i=2}^{n} I\left((f_{i}-f_{i-1}) (y_{i}-y_{i-1}) > 0\right)\]
Latex equation code:
\text{PCD}(y, \hat{y}) = \frac{1}{n-1} \sum_{i=2}^{n} I\left((f_{i}-f_{i-1}) (y_{i}-y_{i-1}) > 0\right)
where $f_i$ is the predicted value at time $i$, $y_i$ is the actual value at time $i$, $n$ is the total number of predictions, and $I(cdot)$ is the
indicator function which equals 1 if the argument is true and 0 otherwise. + Best possible score is 1.0, bigger value is better . Range = [0, 1] + The Prediction of Change in Direction (PCD) metric is used to evaluate the performance of regression models on detecting changes in the direction of a target variable.
Example to use PCD metric:
from numpy import array
from permetrics.regression import RegressionMetric
## For 1-D array
y_true = array([3, -0.5, 2, 7])
y_pred = array([2.5, 0.0, 2, 8])
evaluator = RegressionMetric(y_true, y_pred)
print(evaluator.prediction_of_change_in_direction())
## For > 1-D array
y_true = array([[0.5, 1], [-1, 1], [7, -6]])
y_pred = array([[0, 2], [-1, 2], [8, -5]])
evaluator = RegressionMetric(y_true, y_pred)
print(evaluator.PCD(multi_output="raw_values"))