How to use Metrics Package In R Language
R provides us smape() function defined under the Metrics package using which we can calculate SMAPE. The function has the following syntax:
Syntax:
smape(actual, forecast)
Parameters:
- actual: It represents a vector containing actual values.
- forecasted: It represents a vector containing forecasted values
Return Type:
Returns the SMAPE error between actual and predicted values.
Example:
In this program, we have created two vectors; actual and forecast for the model. Then using smape() function we are calculating symmetric mean absolute percentage error.
R
# Importing librarylibrary(Metrics)# Defining a vector containing actual valuesactual <- c(10, 33, 42, 18, 19, 21, 22)# Defining a vector containing forecasted# valuesforecast <- c(16, 19, 24, 27, 25, 36, 48)# Determine SMAPEsmape(actual, forecast) |
Output:
Thus symmetric mean absolute percentage error for this model comes out to be equal to 49.819%.
How to Calculate SMAPE in R
SMAPE stands for symmetric mean absolute percentage error. It is an accuracy measure and is used to determine the predictive accuracy of models that are based on relative errors. The relative error is computed as:
relative error = x / y
Where x is the absolute error and y is the magnitude of exact value
SMAPE has both lower bound and upper bound values (in contrast to the mean absolute percentage error). SMAPE can be calculated easily using the following formula:
SMAPE = (1 / n) * Ī£(|forecast_value ā actual_value| / ((|actual_value| + |forecast_value|) / 2) * 100
Here,
Ī£ : a symbol that means āsumā
|| : represent the absolute value
n : It represents the sample size
actual_value: It represents the actual data value
forecast_value : It represents the forecasted data value
This article focuses on how we can calculate the SMAPE in R:
Calculating SMAPE in R:
There are two ways using which we can calculate SMAPE in R. These methods are described below in detail:
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