Alternative To Reduce The Error Percentage
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 measure of prediction accuracy of a forecasting method in statistics, for example
Percent Difference Formula
in trend estimation. It usually expresses accuracy as a percentage, and is defined by the percent difference physics formula: M = 100 n ∑ t = 1 n | A t − F t A t | , {\displaystyle {\mbox{M}}={\frac {100}{n}}\sum mean absolute percentage error _{t=1}^{n}\left|{\frac {A_{t}-F_{t}}{A_{t}}}\right|,} where At is the actual value and Ft is the forecast value. The difference between At and Ft is divided by the Actual value At again. The absolute value in this calculation is summed for every
Mean Absolute Percentage Error Excel
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 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
Percent Difference Chemistry
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 alternative, each actual value (At) of the series in the original formula can be replaced by the average of all actual values (Ä€t) of that series. This alternative is still being used for measuring the performance of models that forecast spot electricity prices.[2] Note that this is the same as dividing the sum of absolute differences by the sum of actual values, and is sometimes referred to as WAPE. Issues[edit] While MAP
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 percent difference vs percent change pros and cons of each and discusses their suitability under a variety of circumstances. The MAPE
Mean Percentage Error
The MAPE (Mean Absolute Percent Error) measures the size of the error in percentage terms. It is calculated as the average of the percent difference definition unsigned percentage 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 https://en.wikipedia.org/wiki/Mean_absolute_percentage_error when you don’t 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, http://www.forecastpro.com/Trends/forecasting101August2011.html when the Actual value is 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
here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more http://math.stackexchange.com/questions/677852/how-to-calculate-relative-error-when-true-value-is-zero about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Mathematics Questions Tags Users Badges Unanswered Ask Question _ Mathematics Stack Exchange is a question and answer site for people studying math at any level and professionals in related fields. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The percent difference best answers are voted up and rise to the top How to calculate relative error when true value is zero? up vote 10 down vote favorite 3 How do I calculate relative error when the true value is zero? Say I have $x_{true} = 0$ and $x_{test}$. If I define relative error as: $\text{relative error} = \frac{x_{true}-x_{test}}{x_{true}}$ Then the relative error is always undefined. If instead I use the mean absolute percentage definition: $\text{relative error} = \frac{x_{true}-x_{test}}{x_{test}}$ Then the relative error is always 100%. Both methods seem useless. Is there another alternative? statistics share|cite|improve this question asked Feb 15 '14 at 22:41 okj 941818 1 you need a maximum for that.. –Seyhmus Güngören Feb 15 '14 at 23:06 1 Simple and interesting question, indeed. Could you tell in which context you face this situation ? Depending on your answer, there are possible alternatives. –Claude Leibovici Feb 16 '14 at 6:24 1 @ClaudeLeibovici: I am doing a parameter estimation problem. I know the true parameter value ($x_{true}$), and I have simulation data from which I infer an estimate of the parameter ($x_{test}$). I want to quantify the error, and it seems that for my particular case relative error is more meaningful than absolute error. –okj Feb 17 '14 at 14:05 1 What about $\text{error} = 2 \frac{x_{true}-x_{test}}{x_{true}+x_{test}}$ if it is for an a posteriori analysis ? –Claude Leibovici Feb 17 '14 at 14:16 1 @okj. I am familiar with this situation. Either use the classical relative error and return $NaN$ if $x_{true}=0$ either adopt this small thing. It is always the same problem with that. You also can add a translation