Absolute Mean Percentage Error
Contents |
may be challenged and removed. (December 2009) (Learn how and when to remove this template message) The mean absolute percentage error (MAPE), also known as mean absolute percentage deviation (MAPD), is a high or low mean absolute percentage error measure of prediction accuracy of a forecasting method in statistics, for example in mape mean absolute error trend estimation. It usually expresses accuracy as a percentage, and is defined by the formula: M = 100 n percent of mean absolute error ∑ t = 1 n | A t − F t A t | , {\displaystyle {\mbox{M}}={\frac {100}{n}}\sum _{t=1}^{n}\left|{\frac {A_{t}-F_{t}}{A_{t}}}\right|,} where At is the actual value and Ft is the forecast value.
Mape Mean Percentage Error
The difference between At and Ft is divided by the Actual value At again. The absolute value in this calculation is summed for every forecasted point in time and divided by the number of fitted pointsn. Multiplying by 100 makes it a percentage error. Although the concept of MAPE sounds very simple and convincing, it has major drawbacks in practical application [1] It cannot be mape percentage error used if there are zero values (which sometimes happens for example in demand data) because there would be a division by zero. For forecasts which are too low the percentage error cannot exceed 100%, but for forecasts which are too high there is no upper limit to the percentage error. When MAPE is used to compare the accuracy of prediction methods it is biased in that it will systematically select a method whose forecasts are too low. This little-known but serious issue can be overcome by using an accuracy measure based on the ratio of the predicted to actual value (called the Accuracy Ratio), this approach leads to superior statistical properties and leads to predictions which can be interpreted in terms of the geometric mean.[1] Contents 1 Alternative MAPE definitions 2 Issues 3 See also 4 External links 5 References Alternative MAPE definitions[edit] Problems can occur when calculating the MAPE value with a series of small denominators. A singularity problem of the form 'one divided by zero' and/or the creation of very large changes in the Absolute Percentage Error, caused by a small deviation in error, can occur. As an alte
Interpretation of these statistics can be tricky, particularly when working with low-volume data or when trying to assess accuracy across multiple items (e.g., SKUs, locations, customers, etc.). This installment of Forecasting 101 surveys common error measurement statistics, examines the pros and mape mean percentage cons of each and discusses their suitability under a variety of circumstances. The MAPE The MAPE
Ape Error Means
(Mean Absolute Percent Error) measures the size of the error in percentage terms. It is calculated as the average of the unsigned percentage
Ape Error Mape
error, as shown in the example below: Many organizations focus primarily on the MAPE when assessing forecast accuracy. Most people are comfortable thinking in percentage terms, making the MAPE easy to interpret. It can also convey information when you don’t https://en.wikipedia.org/wiki/Mean_absolute_percentage_error know the item’s demand volume. For example, telling your manager, "we were off by less than 4%" is more meaningful than saying "we were off by 3,000 cases," if your manager doesn’t know an item’s typical demand volume. The MAPE is scale sensitive and should not be used when working with low-volume data. Notice that because "Actual" is in the denominator of the equation, the MAPE is undefined when Actual demand is zero. Furthermore, when the Actual value is http://www.forecastpro.com/Trends/forecasting101August2011.html not zero, but quite small, the MAPE will often take on extreme values. This scale sensitivity renders the MAPE close to worthless as an error measure for low-volume data. The MAD The MAD (Mean Absolute Deviation) measures the size of the error in units. It is calculated as the average of the unsigned errors, as shown in the example below: The MAD is a good statistic to use when analyzing the error for a single item. However, if you aggregate MADs over multiple items you need to be careful about high-volume products dominating the results--more on this later. Less Common Error Measurement Statistics The MAPE and the MAD are by far the most commonly used error measurement statistics. There are a slew of alternative statistics in the forecasting literature, many of which are variations on the MAPE and the MAD. A few of the more important ones are listed below: MAD/Mean Ratio. The MAD/Mean ratio is an alternative to the MAPE that is better suited to intermittent and low-volume data. As stated previously, percentage errors cannot be calculated when the actual equals zero and can take on extreme values when dealing with low-volume data. These issues become magnified when you start to average MAPEs over multiple time series. The MAD/Mean ratio tries to overcome this problem by dividing the MAD by the Mean--essentially rescaling the error to make it comparable across time series of var
Percentage Error (MAPE) Ed Dansereau SubscribeSubscribedUnsubscribe892892 Loading... Loading... Working... Add to Want to watch this again later? Sign in to add this video to a playlist. Sign in Share More Report Need to https://www.youtube.com/watch?v=8cgIb9He5F8 report the video? Sign in to report inappropriate content. Sign in Transcript Statistics 15,288 views 18 Like this video? Sign in to make your opinion count. Sign in 19 2 Don't like this video? Sign in to make your opinion count. Sign in 3 Loading... Loading... Transcript The interactive transcript could not be loaded. Loading... Loading... Rating is available when the percentage error video has been rented. This feature is not available right now. Please try again later. Published on Dec 13, 2012All rights reserved, copyright 2012 by Ed Dansereau Category Education License Standard YouTube License Show more Show less Loading... Advertisement Autoplay When autoplay is enabled, a suggested video will automatically play next. Up next Forecasting - Measurement of error (MAD and MAPE) - mean percentage error Example 2 - Duration: 18:37. maxus knowledge 15,983 views 18:37 3-3 MAPE - How good is the Forecast - Duration: 5:30. Excel Analytics 3,326 views 5:30 Forecasting: Moving Averages, MAD, MSE, MAPE - Duration: 4:52. Joshua Emmanuel 26,135 views 4:52 MFE, MAPE, moving average - Duration: 15:51. East Tennessee State University 29,522 views 15:51 Rick Blair - measuring forecast accuracy webinar - Duration: 58:30. Rick Blair 158 views 58:30 Calculating Forecast Accuracy - Duration: 15:12. MicroCraftTKC 1,713 views 15:12 Forecasting Assignment Part 1: Calculating a Simple Moving Average Forecast in Excel - Duration: 11:43. Lesley Blicker 4,075 views 11:43 Forecast Exponential Smooth - Duration: 6:10. Ed Dansereau 411 views 6:10 Forecasting - Measurement of error MAD - Example 1 - Duration: 20:46. maxus knowledge 4,061 views 20:46 Forecasting MAD/TS/RSFE - Duration: 4:25. Joshua Ates 12,416 views 4:25 Introduction to Mean Absolute Deviation - Duration: 7:47. Rob Christensen 18,377 views 7:47 JEE Physics - Problem related to error Analysis - Duration: 9:16. XLClasses 2,190 views 9:16 Percentage Error and Percentage Difference - Duration: 10:28. Clyde Lettsome 2,700 views 10:28 Forecast Adaptive Exponential Smo