How to calculate MAPE value?

Publish date: 2024-07-03

The Mean Absolute Percentage Error (MAPE) value is calculated by taking the absolute percentage error between actual and predicted values for a time series data set, then averaging these errors across all data points and expressing it as a percentage.

MAPE is a common metric used to evaluate the accuracy of forecasting models, particularly in fields like finance, sales, and supply chain management. Calculating the MAPE value allows businesses to understand how well their forecasting model is performing in predicting future values based on historical data.

To calculate MAPE value, follow these steps:
1. Subtract the predicted value from the actual value for each data point.
2. Divide the absolute value of the difference by the actual value.
3. Sum all the percentage errors.
4. Divide the total by the number of data points.
5. Multiply the result by 100 to get the MAPE value as a percentage.

To illustrate this with an example, let’s say you have a time series data set where the actual values are [100, 200, 300] and the predicted values are [110, 190, 310]. Follow the steps above to calculate the MAPE value.

Calculating MAPE for the first data point:
1. |100 – 110| / 100 = 0.1
2. Absolute Percentage Error = 10%
Continue this process for all data points, then average the absolute percentage errors and multiply by 100 to get the MAPE value.

In this example, the MAPE value would be calculated as follows:
(10% + 5% + 3.33%) / 3 = 6.11%

Therefore, the MAPE value for this time series data set is 6.11%.

Now that we understand how to calculate MAPE value, let’s address some common questions related to this topic.

Table of Contents

FAQs:

1. What is the purpose of calculating MAPE value?

The purpose of calculating MAPE value is to assess the accuracy of a forecasting model by measuring the average percentage error between actual and predicted values.

2. Can MAPE value be negative?

No, MAPE value cannot be negative since it measures the absolute percentage error between actual and predicted values.

3. What does a high MAPE value indicate?

A high MAPE value indicates that the forecasting model has a larger average percentage error, suggesting that the model may not be accurately predicting future values.

4. Is MAPE value always expressed as a percentage?

Yes, MAPE value is always expressed as a percentage since it represents the average percentage error between actual and predicted values.

5. Can MAPE value be used for any type of data?

MAPE value is commonly used for time series data, particularly in fields where forecasting plays a crucial role, such as sales, finance, and supply chain management.

6. How can MAPE value help in decision-making?

By calculating the MAPE value, businesses can assess the accuracy of their forecasting models and make informed decisions based on the reliability of these predictions.

7. What is considered an acceptable MAPE value?

There is no universal threshold for an acceptable MAPE value, as it can vary depending on the industry and specific requirements of the forecasting model. However, lower MAPE values are generally preferred.

8. Can MAPE value be used to compare different forecasting models?

Yes, MAPE value can be used to compare the accuracy of different forecasting models by evaluating their average percentage errors in predicting future values based on historical data.

9. Is MAPE value affected by outliers in the data?

MAPE value can be influenced by outliers in the data, as large deviations between actual and predicted values can significantly impact the average percentage error.

10. How often should MAPE value be recalculated?

MAPE value should be recalculated regularly, especially when there are changes in the underlying data or forecasting model, to ensure that the accuracy of predictions is being properly evaluated.

11. Can MAPE value be used for short-term forecasting?

MAPE value can be used for short-term forecasting as well, provided that the time series data is sufficient to evaluate the accuracy of predictions within a shorter timeframe.

12. Are there any limitations to using MAPE value?

One limitation of using MAPE value is that it does not account for the direction of errors, only their magnitude, which may be a drawback when evaluating the performance of forecasting models.

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