# ARIMA model with huge MAPE value

I have a question about the role of the MAPE in the ARIMA model optimization.

For a daily time series I have found that the best model (using the Box-Jenkins approach) is an ARIMA(7,0,7)(0,0,0). If I check the ACF and PACF of the model residuals, I see that there is no more information I can extract; see the right panel of the picture below.

However, the MAPE value is as high as 18209.16. To me this sounds too high according to its definition.

Have I missed something? Can I accept this ARIMA model even if it has such a high MAPE?

By the way, I am getting similar results if I use auto.arima; MAPE is always big.

Citing Rob J. Hyndman "Measuring forecast accuracy" (2014),

Measures based on percentage errors have the disadvantage of being infinite or undefined if $y_t = 0$ for any observation in the test set, and having extreme values when any $y_t$ is close to zero.

Since the other accuracy measures seem OK (e.g. MASE well below 1), it might be the specifics of the data rather than a faulty model that is producing the very high MAPE.

Also note that (again citing from the same source)

[a]nother problem with percentage errors <...> is that they assume a scale based on quantity. If $y_t$ is measured in dollars, or kilograms, or some other quantity, percentages make sense. On the other hand, a percentage error makes no sense when measuring the accuracy of temperature forecasts on the Fahrenheit or Celsius scales, because these are not measuring a quantity.

So perhaps you could discard MAPE right at the start based on what your raw data is measuring.

Daily data is often better modeled with a mixture of ARIMA and deterministic effects. Day-of-the-week , month-of-the-year , holiday effects etc. while accounting for pulses/level shifts/time trends. Look at http://www.autobox.com/cms/index.php/afs-university/intro-to-forecasting/doc_download/53-capabilities-presentation slide 47 for an example of this.

• Thanks for your answer. When you talk about deterministic effects do you means that I manage as you have described here? [link] (stats.stackexchange.com/questions/48921/…) (similar to page 20). So this should means that I have to clean the data not only using the IQR approach but also considering some "peak" in the data, right ?
– DP78
Aug 15 '16 at 21:16
• The software should handle the cleaning of he data reflecting not only pulses but seasonal pulses, level shifts and time trends ..... Aug 15 '16 at 22:39