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Mean Absolute Scaled Error

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Koehler, who described it as a "generally applicable measurement of forecast accuracy without the problems seen in the other measurements."[1] The mean absolute scaled error has favorable properties when compared to Think of it this way: how large a sample of data would you want in order to estimate a single parameter, namely the mean? It's not too surprising that forecasts deteriorate with increasing horizons, so this may be another reason for a MASE of 1.38. B. (2006). "Another look at measures of forecast accuracy." International Journal of Forecasting volume 22 issue 4, pages 679-688. check over here

Other explanations could, as you say, be different structural breaks, e.g., level shifts or external influences like SARS or 9/11, which would not be captured by the non-causal benchmark models, but Generated Thu, 20 Oct 2016 11:33:36 GMT by s_wx1062 (squid/3.5.20) There is no absolute standard for a "good" value of adjusted R-squared. Please help improve it by replacing them with more appropriate citations to reliable, independent, third-party sources. (April 2011) (Learn how and when to remove this template message) In statistics, the mean https://en.wikipedia.org/wiki/Mean_absolute_scaled_error

Mean Absolute Scaled Error Excel

This is the statistic whose value is minimized during the parameter estimation process, and it is the statistic that determines the width of the confidence intervals for predictions. See also[edit] Mean squared error Mean absolute error Mean absolute percentage error Root-mean-square deviation Test Set References[edit] ^ a b Hyndman, R. Suppose $k$ observations are required to produce a reliable forecast. For example, a percentage error makes no sense when measuring the accuracy of temperature forecasts on the Fahrenheit or Celsius scales.

How these are computed is beyond the scope of the current discussion, but suffice it to say that when you--rather than the computer--are selecting among models, you should show some preference Thus, no future observations can be used in constructing the forecast. Hyndman and Koehler (2006) recommend that the sMAPE not be used. Interpreting Mase doi:10.1016/j.ijforecast.2015.03.008. ^ a b c Hyndman, R.

The confidence intervals widen much faster for other kinds of models (e.g., nonseasonal random walk models, seasonal random trend models, or linear exponential smoothing models). Mean Absolute Scaled Error Interpretation If there is evidence that the model is badly mis-specified (i.e., if it grossly fails the diagnostic tests of its underlying assumptions) or that the data in the estimation period has You must estimate the seasonal pattern in some fashion, no matter how small the sample, and you should always include the full set, i.e., don't selectively remove seasonal dummies whose coefficients Find first non-repetitive char in a string UV lamp to disinfect raw sushi fish slices Triangles tiling on a hexagon Create a 5x5 Modulo Grid Publishing a mathematical research article on

In contrast, the MAPE and median absolute percentage error (MdAPE) fail both of these criteria, while the "symmetric" sMAPE and sMdAPE[4] fail the second criterion. Mase Excel We recommend upgrading to the latest Safari, Google Chrome, or Firefox. The confidence intervals for some models widen relatively slowly as the forecast horizon is lengthened (e.g., simple exponential smoothing models with small values of "alpha", simple moving averages, seasonal random walk Download PDFs Help Help Linear regression models Notes on linear regression analysis (pdf file) Introduction to linear regression analysis Mathematics of simple regression Regression examples · Baseball batting averages ·

Mean Absolute Scaled Error Interpretation

How long could the sun be turned off without overly damaging planet Earth + humanity? The other standard methods, like ForecastPro, ETS etc. Mean Absolute Scaled Error Excel The mean absolute scaled error (MASE) is another relative measure of error that is applicable only to time series data. Mean Absolute Scaled Error In R In such cases, you have to convert the errors of both models into comparable units before computing the various measures.

When $h=1$, this gives the same procedure as outlined above. ‹ 2.4 Transformations and adjustments up 2.6 Residual diagnostics › Book information About this bookFeedback on this book Buy a printed http://threadspodcast.com/mean-absolute/mean-absolute-prediction-error.html Interpretability: The mean absolute scaled error can be easily interpreted, as values greater than one indicate that in-sample one-step forecasts from the naïve method perform better than the forecast values under Conversely, it is greater than one if the forecast is worse than the average naïve forecast computed on the training data. The authors (all of them are expert forecasters and very active in the IIF - no snake oil salesmen here) are quite capable of applying standard forecasting algorithms or forecasting software, Mean Absolute Scaled Error Matlab

the bottom line is that you should put the most weight on the error measures in the estimation period--most often the RMSE (or standard error of the regression, which is RMSE more stack exchange communities company blog Stack Exchange Inbox Reputation and Badges sign up log in tour help Tour Start here for a quick overview of the site Help Center Detailed Retrieved 2016-05-15. ^ a b Hyndman, Rob et al, Forecasting with Exponential Smoothing: The State Space Approach, Berlin: Springer-Verlag, 2008. http://threadspodcast.com/mean-absolute/mean-absolute-relative-error.html Training and test sets It is important to evaluate forecast accuracy using genuine forecasts.

Would it be easy or hard to explain this model to someone else? Mase In R The mean absolute percentage error (MAPE) is also often useful for purposes of reporting, because it is expressed in generic percentage terms which will make some kind of sense even to in the top row -- are hard to predict: no surprise. (Athanasopoulos, Hyndman, Song and Wu, The Tourism Forecasting Competition (2011, 23p) used 112 of the 518 yearly series, but I

A perfect fit can always be obtained by using a model with enough parameters.

Repeat the above step for $i=1,2,\dots,T-k$ where $T$ is the total number of observations. This is especially problematic for data sets whose scales do not have a meaningful 0, such as temperature in Celsius or Fahrenheit, and for intermittent demand data sets, where y t J. Another Look At Measures Of Forecast Accuracy However, other procedures in Statgraphics (and most other stat programs) do not make life this easy for you. (Return to top of page) There is no absolute criterion for a "good"

Compute the forecast accuracy measures based on the errors obtained. Note that the Seasonal Naive forecast was also included in the benchmark and had an even higher MASE. Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. have a peek at these guys Asking for a written form filled in ALL CAPS Can't a user change his session information to impersonate others?

Symmetry: The mean absolute scaled error penalizes positive and negative forecast errors equally, and penalizes errors in large forecasts and small forecasts equally. Various other criteria do not fit, as they do not imply the relevant moment properties, and this is illustrated in some simulation experiments.KeywordsForecast accuracy; Forecast error measures; Statistical testingCorrespondence to: Econometric Although the confidence intervals for one-step-ahead forecasts are based almost entirely on RMSE, the confidence intervals for the longer-horizon forecasts that can be produced by time-series models depend heavily on the Etymologically, why do "ser" and "estar" exist?

All rights reserved.Philip Hans Franses is a Professor of Applied Econometrics and Professor of Marketing Research, both affiliated with the Erasmus School of Economics. doi:10.1016/0169-2070(93)90079-3. ^ a b c d "2.5 Evaluating forecast accuracy | OTexts". Browse other questions tagged time-series forecasting accuracy mase or ask your own question. It is included here only because it is widely used, although we will not use it in this book.

Hence, the naïve forecast is recommended when using time series data.) The mean absolute scaled error is simply [ \text{MASE} = \text{mean}(|q_{j}|). ] Similarly, the mean squared scaled error (MSSE) can Of course, this question is not specific to the particular forecasting competition. This scale-free error metric "can be used to compare forecast methods on a single series and also to compare forecast accuracy between series. Please enable JavaScript to use all the features on this page.

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