The symmetric mean absolute percentage error (SMAPE) is used to measure the predictive accuracy of models. It is calculated as:
SMAPE = (1/n) * Σ(|forecast – actual| / ((|actual| + |forecast|)/2) * 100
where:
- Σ – a symbol that means “sum”
- n – sample size
- actual – the actual data value
- forecast – the forecasted data value
The smaller the value for SMAPE, the better the predictive accuracy of a given model.
This tutorial explains two different methods you can use to calculate SMAPE in R.
Method 1: Use smape() from the Metrics Package
One way to calculate SMAPE in R is to use the smape() function from the Metrics package:
library(Metrics) #define actual values actual #define forecasted values forecast #calculate SMAPE smape(actual, forecast) [1] 0.1245302
We can see that the symmetric mean absolute percentage error for this model is 12.45%.
Method 2: Write Your Own Function
Another way to calculate SMAPE is to create our own function as follows:
find_smape function(a, f) {
return (1/length(a) * sum(2*abs(f-a) / (abs(a)+abs(f))*100))
}
We can then use this function to calculate the SMAPE between a vector of actual values and forecasted values:
#define actual values
actual #define forecasted values
forecast #calculate SMAPE
find_smape(actual, forecast)
[1] 12.45302
Once again the SMAPE turns out to be 12.45%, which matches the results from the previous example.
Additional Resources
How to Calculate MAPE in R
How to Calculate MAD in R
How to Calculate MAE in R
How to Calculate RMSE in R
How to Calculate MSE in R