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FAQs
Is SMAPE a good metric? ›
Advantages of SMAPE:
Expressed as a percentage. Safer metric to use when there is a lot of sparsity in the data. Unlike MAPE which has no limits, it has both the lower (0%) and the upper (200%) bounds.
The resulting score ranges between 0 and 1, where a score of 0 indicates a perfect match between the actual and predicted values, and a score of 1 indicates no match at all. A smaller value of SMAPE is better, and it is often multiplied by 100% to obtain the percentage error.
What is the range of the SMAPE value? ›smape ( Tensor ): A tensor with non-negative floating point smape value between 0 and 1.
What is a good SMAPE for forecasting? ›The resulting SMAPE values range from 0% to 100%, with lower values indicating higher accuracy. A SMAPE value of 0% signifies a perfect alignment between the forecasted and actual values, while a value of 100% implies no similarity between the two.
What does SMAPE tell us? ›Mean absolute percentage error measures the average magnitude of error produced by a model, or how far off predictions are on average. A MAPE value of 20% means that the average absolute percentage difference between the predictions and the actuals is 20%.
What is the formula for the SMAPE metric system? ›The SMAPE formula is:SMAPE = 1/n x Σ(|Forecast - Actual| / ((|Actual + Forecast|) / 2) x 100Where: n is the sample size. Σ is a symbol that means the sum. |x| are the symbols denoting absolute value, which is the distance away from zero.
Is it better to have a higher or lower MAPE? ›Lower MAPE values indicate better accuracy, with a value of 0 indicating a perfect match between predicted and observed values. ... Background: Buffalo milk production in India plays a significant role in the global dairy market, with a rich history deeply intertwined with the country's economy and culture.
When to use SMAPE? ›If we underpredict, the sMAPE will be higher compared to overprediction, because the difference raised with Predicted in both the cases. Finally, it is safer to use sMAPE during cases, if there is more sparsity in data else, MAPE is a good metric to check the accuracy.
Can SMAPE be negative? ›SMAPE output is a non-negative floating point between 0 and 1. Best result is 0.0 .
What is a good level of forecast accuracy? ›What is a Good Forecast Accuracy Percentage? While the goal is always 100%, this can easily be seen as far out of reach. It is thus widely suggested that any percentage north of 70% is a good forecast accuracy percentage. However, this benchmark does have certain factors affecting it, such as industry and demand.
What is a good error range? ›
The acceptable margin of error usually falls between 4% and 8% at the 95% confidence level.
When to use sMAPE? ›If we underpredict, the sMAPE will be higher compared to overprediction, because the difference raised with Predicted in both the cases. Finally, it is safer to use sMAPE during cases, if there is more sparsity in data else, MAPE is a good metric to check the accuracy.
What is the best metric for forecasting? ›Mean Absolute Percentage Error (MAPE)
This is often the baseline metric used to measure most forecasting models. Pros: Easy to interpret. Scale independent, so can compare across different time series.
Take this as a percentage of the average of y, and you have MAPE. Generally speaking, a value below 10% is great, 10% to 20% is still good, and above 50% means your model is inaccurate because you're wrong more than you're right.
What are the disadvantages of mean absolute percentage error? ›MAPE is an error metric that is easy to understand because it provides the error in terms of percentages. A lower value of MAPE implies a higher accuracy of the model. A significant disadvantage of MAPE is that it produces undefined values when the actual values are 0, which is a common occurrence in some fields.