Average Percentage Error
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the quantity being forecast. The formula for the mean percentage error is MPE = 100 % mean percentage error n ∑ t = 1 n a t − f t average percentage error calculator a t {\displaystyle {\text{MPE}}={\frac {100\%}{n}}\sum _{t=1}^{n}{\frac {a_{t}-f_{t}}{a_{t}}}} where at is the actual value of the quantity average relative error being forecast, ft is the forecast, and n is the number of different times for which the variable is forecast. Because actual rather than absolute values average standard deviation of the forecast errors are used in the formula, positive and negative forecast errors can offset each other; as a result the formula can be used as a measure of the bias in the forecasts. A disadvantage of this measure is that it is undefined whenever a single actual value is zero.
Average Percent Error Chemistry
See also[edit] Percentage error Mean absolute percentage error Mean squared error Mean squared prediction error Minimum mean-square error Squared deviations Peak signal-to-noise ratio Root mean square deviation Errors and residuals in statistics References[edit] Khan, Aman U.; Hildreth, W. Bartley (2003). Case studies in public budgeting and financial management. New York, N.Y: Marcel Dekker. ISBN0-8247-0888-1. Waller, Derek J. (2003). Operations Management: A Supply Chain Approach. Cengage Learning Business Press. ISBN1-86152-803-5. Retrieved from "https://en.wikipedia.org/w/index.php?title=Mean_percentage_error&oldid=723517980" Categories: Summary statistics Navigation menu Personal tools Not logged inTalkContributionsCreate accountLog in Namespaces Article Talk Variants Views Read Edit View history More Search Navigation Main pageContentsFeatured contentCurrent eventsRandom articleDonate to WikipediaWikipedia store Interaction HelpAbout WikipediaCommunity portalRecent changesContact page Tools What links hereRelated changesUpload fileSpecial pagesPermanent linkPage informationWikidata itemCite this page Print/export Create a bookDownload as PDFPrintable version Languages Add links This page was last modified on 3 June 2016, at 14:20. Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply
Example: I estimated 260 people, but 325 came. 260 − 325 = −65, ignore the "−" sign, so my error is 65 "Percentage Error": show the error as a percent
Percent Error Accepted Value
of the exact value ... so divide by the exact value and make mean average percentage error excel it a percentage: 65/325 = 0.2 = 20% Percentage Error is all about comparing a guess or estimate to an mean percentage error example exact value. See percentage change, difference and error for other options. How to Calculate Here is the way to calculate a percentage error: Step 1: Calculate the error (subtract one value form the other) ignore https://en.wikipedia.org/wiki/Mean_percentage_error any minus sign. Step 2: Divide the error by the exact value (we get a decimal number) Step 3: Convert that to a percentage (by multiplying by 100 and adding a "%" sign) As A Formula This is the formula for "Percentage Error": |Approximate Value − Exact Value| × 100% |Exact Value| (The "|" symbols mean absolute value, so negatives become positive) Example: https://www.mathsisfun.com/numbers/percentage-error.html I thought 70 people would turn up to the concert, but in fact 80 did! |70 − 80| |80| × 100% = 10 80 × 100% = 12.5% I was in error by 12.5% Example: The report said the carpark held 240 cars, but we counted only 200 parking spaces. |240 − 200| |200| × 100% = 40 200 × 100% = 20% The report had a 20% error. We can also use a theoretical value (when it is well known) instead of an exact value. Example: Sam does an experiment to find how long it takes an apple to drop 2 meters. The theoreticalvalue (using physics formulas)is 0.64 seconds. But Sam measures 0.62 seconds, which is an approximate value. |0.62 − 0.64| |0.64| × 100% = 0.02 0.64 × 100% = 3% (to nearest 1%) So Sam was only 3% off. Without "Absolute Value" We can also use the formula without "Absolute Value". This can give a positive or negative result, which may be useful to know. Approximate Value − Exact Value × 100% Exact Value Example: They forecast 20 mm of rain, but we really got 25 mm. 20 − 25 25
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 http://www.forecastpro.com/Trends/forecasting101August2011.html error measurement statistics, examines the pros and cons of each and discusses their suitability under a variety of circumstances. The MAPE The MAPE (Mean Absolute Percent Error) measures the size of the error in percentage terms. It is calculated as the average of the 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, percentage error making the MAPE easy to interpret. It can also convey information 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 average percentage error in the denominator of the equation, the MAPE is undefined when Actual demand is zero. Furthermore, 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