# Metrics ------------------------------------------------------------------------ source ### MAPE ``` python def MAPE( y_true:ArrayLike, # The actual values. y_pred:ArrayLike, # The predicted values. )->float: # The MAPE value rounded to 2 decimal places. ``` *Calculate the Mean Absolute Percentage Error (MAPE) between actual and predicted values.*
Type Details
y_true ArrayLike The actual values.
y_pred ArrayLike The predicted values.
Returns float The MAPE value rounded to 2 decimal places.
------------------------------------------------------------------------ source ### SMAPE ``` python def SMAPE( y_true:ArrayLike, # The actual values. y_pred:ArrayLike, # The predicted values. )->float: # The SMAPE value rounded to 2 decimal places. ``` *Calculate the Symmetric Mean Absolute Percentage Error (SMAPE) between actual and predicted values.*
Type Details
y_true ArrayLike The actual values.
y_pred ArrayLike The predicted values.
Returns float The SMAPE value rounded to 2 decimal places.
------------------------------------------------------------------------ source ### WMAPE ``` python def WMAPE( y_true:ArrayLike, # The actual values. y_pred:ArrayLike, # The predicted values. )->float: # The WMAPE value. ``` *“* Calculate the Weighted Mean Absolute Percentage Error (WMAPE) between actual and predicted values.
Type Details
y_true ArrayLike The actual values.
y_pred ArrayLike The predicted values.
Returns float The WMAPE value.
------------------------------------------------------------------------ source ### MAE ``` python def MAE( y_true:ArrayLike, # The actual values. y_pred:ArrayLike, # The predicted values. )->float: # The MAE value. ``` *Calculate mean absolute error (MAE).*
Type Details
y_true ArrayLike The actual values.
y_pred ArrayLike The predicted values.
Returns float The MAE value.
------------------------------------------------------------------------ source ### MSE ``` python def MSE( y_true:ArrayLike, # The actual values. y_pred:ArrayLike, # The predicted values. )->float: # The MSE value. ``` *Calculate mean squared error (MSE).*
Type Details
y_true ArrayLike The actual values.
y_pred ArrayLike The predicted values.
Returns float The MSE value.
------------------------------------------------------------------------ source ### RMSE ``` python def RMSE( y_true:ArrayLike, # The actual values. y_pred:ArrayLike, # The predicted values. )->float: # The RMSE value. ``` *Calculate Root Mean Square Error (RMSE).*
Type Details
y_true ArrayLike The actual values.
y_pred ArrayLike The predicted values.
Returns float The RMSE value.
------------------------------------------------------------------------ source ### SRMSE ``` python def SRMSE( y_true:ArrayLike, # The actual values. y_pred:ArrayLike, # The predicted values. y_train:ArrayLike, # The training values used for scaling. )->float: # The SRMSE value. ``` *Calculate Scaled Root Mean Square Error (SRMSE).*
Type Details
y_true ArrayLike The actual values.
y_pred ArrayLike The predicted values.
y_train ArrayLike The training values used for scaling.
Returns float The SRMSE value.
------------------------------------------------------------------------ source ### RMSSE ``` python def RMSSE( y_true:ArrayLike, # The actual values. y_pred:ArrayLike, # The predicted values. y_train:ArrayLike, # The training values used for scaling. )->float: # The RMSSE value. ``` *Calculate Root Mean Squared Scaled Error (RMSSE).*
Type Details
y_true ArrayLike The actual values.
y_pred ArrayLike The predicted values.
y_train ArrayLike The training values used for scaling.
Returns float The RMSSE value.
------------------------------------------------------------------------ source ### MASE ``` python def MASE( y_true:ArrayLike, # The actual values. y_pred:ArrayLike, # The predicted values. y_train:ArrayLike, # The training values used for scaling. )->float: # The MASE value. ``` *Calculate Mean Absolute Scaled Error (MASE)*
Type Details
y_true ArrayLike The actual values.
y_pred ArrayLike The predicted values.
y_train ArrayLike The training values used for scaling.
Returns float The MASE value.
------------------------------------------------------------------------ source ### CFE ``` python def CFE( y_true:ArrayLike, # The actual values. y_pred:ArrayLike, # The predicted values. )->float: ``` *Calculate Cumulative Forecast Error (CFE). It is the cumulative sum of the differences between actual and predicted values.*
Type Details
y_true ArrayLike The actual values.
y_pred ArrayLike The predicted values.
Returns float
------------------------------------------------------------------------ source ### CFE_ABS ``` python def CFE_ABS( y_true:ArrayLike, # The actual values. y_pred:ArrayLike, # The predicted values. )->float: # The absolute CFE value. ``` *Calculate Absolute Cumulative Forecast Error (CFE_ABS). It is the absolute value of the cumulative sum of the differences between actual and predicted values.*
Type Details
y_true ArrayLike The actual values.
y_pred ArrayLike The predicted values.
Returns float The absolute CFE value.