Metrics

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MAPE

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.

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SMAPE

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.

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WMAPE

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.

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MAE

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.

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MSE

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.

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RMSE

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.

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SRMSE

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.

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RMSSE

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.

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MASE

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.

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CFE

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

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CFE_ABS

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.